ECE Colloquium

The ECE Departmental Colloquium hosts talks across the spectrum of Electrical and Computer Engineering and related fields. The Colloquium series contains a mix of presentations from faculty within Rutgers as well as internationally distinguished speakers hosted by the department.

The current colloquium schedule is listed below. You can subscribe to the colloquium calendar using   iCal

You can also subscribe to the colloquium calendar using   Google Calendar

Announcements for the Colloquium are sent out to all ECE graduate students, staff, and faculty. If you are outside the department and wish to get announcements you may add yourself to the   Rutgers Mailman list .

Dr. Maryam Mehri Dehnavi,
Rutgers University

Wednesday, April 26, 2017 - 10:00am - 11:00am

CoRE Building Lecture Hall

Abstract: Computational scientists often encounter problems requiring the solution to large sparse systems. To enable fast and accurate simulations, the execution time of these solvers needs to be accelerated on modern high-performance architectures. In this talk, I will show that through mathematical reformulation and algorithm re-engineering, memory access patterns in iterative sparse solvers can be restructured to significantly improve their performance on parallel architectures. I will also discuss our recent advances in building domain-specific compilers for sparse matrix computations to automatically generate fast code for sparse numerical methods

Bio: Dr. Maryam Mehri Dehnavi is an Assistant Professor in the Electrical and Computer Engineering Department at Rutgers University. Her research focuses on high-performance computing and domain-specific compiler design. Previously she was a postdoctoral researcher at MIT with the Supertech research group. She received her Ph.D. in Electrical and Computer Engineering from McGill University in 2012.

Dr. Rene Vidal, Johns Hopkins University

Wednesday, April 19, 2017 - 10:00am - 11:00am

CoRE Building Lecture Hall

Speaker: Rene Vidal, Johns Hopkins University

Title: Global Optimality in Matrix Factorization and Deep Learning

Abstract: The past few years have seen a dramatic increase in the performance of recognition systems thanks to the introduction of deep networks for representation learning. However, the mathematical reasons for this success remain elusive. A key issue is that the neural network training problem is nonconvex, hence optimization algorithms may not return a global minima. Building on ideas from convex relaxations of matrix factorizations, in this talk I will present a general framework which allows for the analysis of a wide range of non-convex factorization problems – including matrix factorization, tensor factorization, and deep neural network training. In particular, I will present sufficient conditions under which a local minimum of the non-convex optimization problem is a global minimum and show that if the size of the factorized variables is large enough then from any initialization it is possible to find a global minimizer using a local descent algorithm. Joint work with Ben Haeffele.

Bio: Professor Vidal received his B.S. degree in Electrical Engineering from the Pontificia Universidad Católica de Chile in 1997 and his M.S. and Ph.D. degrees in Electrical Engineering and Computer Sciences from the University of California at Berkeley in 2000 and 2003, respectively. In 2004 he joined the Johns Hopkins University, where he is currently a Professor in the Center for Imaging Science and the Department of Biomedical Engineering. Dr. Vidal is co-author of the book “Generalized Principal Component Analysis” (2016), co-editor of the book “Dynamical Vision” (2006), and co-authored of more than 200 articles in machine learning, computer vision, biomedical image analysis, hybrid systems, robotics and signal processing. Dr. Vidal is Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence, the SIAM Journal on Imaging Sciences, Computer Vision and Image Understanding, and Medical Image Analysis. He has been Program Chair for ICCV 2015 and CVPR 2014, and Area Chair for all major conferences in machine learning, computer vision, and medical image analysis. Dr. Vidal has received many awards for his work including the 2012 J.K. Aggarwal Prize, the 2009 ONR Young Investigator Award, the 2009 Sloan Research Fellowship, the 2005 NFS CAREER Award, and best paper awards at in computer vision (ICCV-3DRR 2013, PSIVT 2013, ECCV 2004), controls (CDC 2012, CDC 2011) and medical robotics (MICCAI 2012). Dr. Vidal was elected fellow of the IEEE in 2014 and fellow of the IAPR in 2016.

Dr. Pei Zhang, Carnegie Mellon University

Wednesday, April 12, 2017 - 10:00am - 11:00am

CoRE Building Lecture Hall

Speaker: Pei Zhang, Carnegie Mellon University, Silicon Valley

Time: 10:00 AM - 11:00 AM

Location: CoRE Lecture Hall

Title: Physical Knowledge Discovery in Cyber-Physical Systems

Abstract: In many sensing scenarios, direct sensing of desired events is difficult and often impossible. This is often due to deployment difficulties, lack of available sensors, and cost of maintenance. This talk will explore indirect sensing framework that infers information from physical inertial sensing. This approach reduces the number of per-node sensors, reduce the number of devices needed, and utilize physical media information to improve sensing. The talk then explores this framework through three projects. 1) The SensorFly system, a low-cost, miniature aerial sensor network that aims to be autonomous in deployment, maintenance, and adaptation to the environment. By modeling the motion and movement, these sensors collaboratively localize and navigate indoors using inertial sensors. 2) Landing the sensors inside the building, the same sensor sets can be used to discover occupant information including location, identity, status, etc. 3) The same sensors on the human body, the system can inference muscle activity and fatigue level. The talk will explore the commonalities of data understanding and inferencing.

Bio: Pei Zhang is an associate research professor in the ECE departments at Carnegie Mellon University. He received his bachelor's degree with honors from California Institute of Technology in 2002, and his Ph.D. degree in Electrical Engineering from Princeton University in 2008. While at Princeton University, he developed the ZebraNet system, which is used to track zebras in Kenya. It was the first deployed, wireless, ad- hoc, mobile sensor network. His recent work includes SensorFly (focus on groups of autonomous miniature-helicopter based sensor nodes) and MARS (Muscle Activity Recognition). Beyond research publications, his work has been featured in popular media including CNN, Science Channel, Discovery Channel, CBS News, CNET, Popular Science, BBC Focus, etc. He is also a co-founder of the startup Vibradotech. In addition, he has won several awards including the NSF CAREER, Google faculty awards, Edith and Martin B. Stein Solar Energy Innovation Award, and a member of the Department of Defense Computer Science Studies Panel.

Dr. Yingbin Liang, Syracuse University

Wednesday, April 5, 2017 - 10:00am

CoRE Building Lecture Hall

Speaker: Yingbin Liang, Syracuse University

Location: CoRE Auditorium

Title: Nonconvex Approach for High Dimensional Estimation

Abstract: High dimensional estimation problems, such as phase retrieval, low rank matrix estimation, and blind deconvolution, attracted intensive attention recently due to their wide applications in medical image, signal processing, social networks, etc. Traditional approaches to solving these problems are either empirical, which work well but lack theoretic guarantee; or via convex formulations, which come with performance guarantee but are computationally costly in large dimensions. Nonconvex approaches are recently emerging as a powerful framework to solve such problems, which come with provable performance guarantee and are computationally efficient.

In this talk, I will first introduce general ideas of using nonconvex methods for solving high dimensional estimation problems. I will then focus on the phase retrieval problem to present our recent advancements of nonconvex method. In particular, I will describe our design of a nonconvex objective that yields first-order algorithm outperforming all existing algorithms in both statistical and computational efficiency. I will then present our further design of stochastic algorithms for large-scale phase retrieval with provable convergence guarantee. I will finally conclude my talk with discussion of insights learned from our studies.

Bio:Dr. Yingbin Liang received the Ph.D. degree in Electrical Engineering from the University of Illinois at Urbana-Champaign in 2005. In 2005-2007, she was working as a postdoctoral research associate at Princeton University. In 2008-2009, she was an assistant professor at the University of Hawaii. Since December 2009, she has been on the faculty at Syracuse University, where she is an associate professor. Dr. Liang's research interests include machine learning, statistical signal processing, optimization, information theory, and wireless communication and networks.

Dr. Liang was a Vodafone Fellow at the University of Illinois at Urbana-Champaign during 2003-2005, and received the Vodafone-U.S. Foundation Fellows Initiative Research Merit Award in 2005. She also received the M. E. Van Valkenburg Graduate Research Award from the ECE department, University of Illinois at Urbana-Champaign, in 2005. In 2009, she received the National Science Foundation CAREER Award, and the State of Hawaii Governor Innovation Award. In 2014, she received EURASIP Best Paper Award for the EURASIP Journal on Wireless Communications and Networking. She served as an Associate Editor for the Shannon Theory of the IEEE Transactions on Information Theory during 2013-2015.

Dr. Mark G. Allen, University of Pennsylvania

Wednesday, February 8, 2017 - 10:00am - 11:00am

CoRE Lecture Hall

Title:  MEMS in Medicine: Looking Back and Looking Forward

Abstract: Microelectromechanical systems, or MEMS, trace their history back almost five decades. Since their conception, forward thinkers have considered their use in biomedical applications for the monitoring and ultimately treatment of disease. This presentation will discuss some of the historical foundations of MEMS in medicine, and illustrate their their use through several examples from our own laboratory, including: microneedles for transdermal drug delivery; microfabricated in-vitro interfaces to electrogenic cells; and biodegradable sensors and power sources. A MEMS-based lab-to-approved product, endovascularly-implantable wireless pressure sensors, will then be discussed. The sensors, microfabricated from silica, have no internal power supply or circuitry and wirelessly communicate the pressure of the environment in which they are embedded to an external reader. Two applications of these sensors will be discussed: detecting the pressure within the excluded portion of endovascularly-repaired abdominal aortic aneurysms to monitor for graft failure; and detecting the pressure within the pulmonary artery for patients with congestive heart failure to titrate medication and reduce heart-failure-related hospitalization. The talk will conclude with a discussion of future directions and prospects for MEMS in medicine.

Biography: Mark G. Allen received the B.A. degree in chemistry, the B.S.E. degree in chemical engineering, and the B.S.E. degree in electrical engineering from the University of Pennsylvania, Philadelphia, and the S.M. and Ph.D. degrees from Massachusetts Institute of Technology, Cambridge. In 1989 he joined the faculty of the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, ultimately holding the rank of Regents’ Professor and the J.M. Pettit Professorship in Microelectronics, as well as a joint appointment in the School of Chemical and Biomolecular Engineering. In 2013 he left Georgia Tech to become the Alfred Fitler Moore Professor of Electrical and Systems Engineering and Scientific Director of the Singh Nanotechnology Center at the University of Pennsylvania. His research interests are in the development and the application of new micro- and nanofabrication technologies, as well as MEMS. Dr. Allen has held the posts of Editor-in-Chief of the Journal of Micromechanics and Microengineering, co-chair of the IEEE/ASME MEMS Conference, and is co-founder of multiple MEMS companies, including Cardiomems and Axion Biosystems. A Fellow of the IEEE, Dr. Allen received the 2016 IEEE Daniel P. Noble Award for contributions to research and development, clinical translation, and commercialization of biomedical microsystems.

Dr. C. Michael Wu, Wayne State University
Room: EE-203

Thursday, January 26, 2017 - 10:30am - 11:30am


Metamaterials (MTMs) are artificial electromagnetic materials with novel effective medium properties that may not be available in nature. The concept of metamaterial structures has led to the design of many novel circuits exhibiting component enhancements. One type of metamaterial-based antenna structure is so-called composite right/left-handed transmission line (CRLH-TL) leaky-wave antennas (LWAs). This kind of antenna structure has been shown to offer significant advantages over conventional LWAs. For example, a balanced CRLH-TL LWA is able to achieve continuous backfire-to-endfire frequency-dependent beam scanning with a true broadside beam, good impedance matching over an entire operating band with a simple feeding structure. Utilizing the frequency-space mapping characteristics of CRLH LWAs, the locations of unknown targets can be determined by simply reading the spectral components of the reflected wave. This results in a real time detection scheme since the data acquiring speed is mostly depending on the frequency sweeping speed of signal source, which is typically on the order of milliseconds. Such speed can be further increased if pulse signals are used. Furthermore, the field-of-view of the sensor can be enlarged due to the wide scanning angle provided by CRLH LWAs. Leveraging this unique feature of MTM antennas, various applications including fast 2-D beamforming, real-time remote sensing with large field-of-view that can be used in automotive
radars, and microwave tomography with low complexity and cost can be realized. The next part of this talk will be on negative group delay (NGD) circuits, another kind of artificial materials engineered to exhibit superluminal group velocities. Conventionally, in microwave regime, NGD circuits are realized using bandstop structures with lumped elements, such as parallel RLC resonators; however, they usually have a narrower NGD bandwidth and also lack a systematic design methodology. Toward this end, NGD circuits based on a microwave transversal filter approach are proposed and shown to have a wide bandwidth of NGD with a comprehensive design method. The fully distributed nature of these NGD circuits make them feasible to further operate at high frequency range. Moreover, it will also be demonstrated that this new kind of transversal-filter based NGD circuit can realize non-Foster elements, i.e. negative inductors and/or capacitors, with potentially unconditional stability and reconfigurability, which is expected to have a great impact on realizing stable non-Foster elements in microwave and millimeter wave regimes.It is envisioned by using these artificially engineered metamaterial based antennas and components, next-generation microwave imaging and communication systems, as well as microwave components with enhanced functionalities and performance will be enabled.

Biography: Dr. Chung-Tse Michael Wu is currently an Assistant Professor at the Department of Electrical and Computer Engineering at Wayne State University (WSU), Michigan, USA. His research interests include applied electromagnetics, antennas, passive/active microwave and millimeter-wave components, RF systems and metamaterials. He received his B.S. degree from National Taiwan University (NTU) in 2006. He then received his M.S. and Ph.D. degree in the Department of Electrical Engineering, University of California at Los Angeles (UCLA) in 2009 and 2014, respectively. From September 2008 to June 2014, he worked as a graduate student researcher at the Microwave Electronics Laboratory in UCLA. In 2009, He was a summer intern in Bell Labs, Alcatel-Lucent, Murray Hills, NJ. In 2012, he was a special-joint researcher at Japan Aerospace Exploration Agency (JAXA) in Kanagawa, Japan.

Eugene Chai, NEC Laboratories

Monday, December 12, 2016 - 1:00pm - 2:30pm

CoRE Building Lecture Hall

Title: 5G and the Future Network Society

5G is the vision for a ubiquitous, fully-connected society, that is shaped by seamless knowledge exchange, ubiquitous availability of services and frictionless sharing of experiences. Some of the impacts that such advanced networks have on areas such as medical technology, virtual reality, and social connectivity can already be experienced today. While 5G encompasses the entire network architecture, wireless access technology plays an outsized role in realizing last-mile connectivity to the vast majority of connected devices.

In this talk, I will introduce my contributions towards this 5G vision. Broadly speaking, I aim to develop mainstream solutions that evolve existing broadband infrastructure towards this 5G future. Towards this goal, I will present works from three areas: LTE in the unlicensed spectrum, dynamic spectrum access, and large-scale MIMO. These works are important components of a hyper-connected network where a massive number of embedded and mobile devices operate seamlessly across a myriad of wireless resources. I will also discuss my future plans for wireless sensing and IoT, and how it is an integral part of future broadband networks.

Eugene Chai is a researcher in the Mobile Communications and Networking department of NEC Laboratories America. He received his M.S. and Ph.D. from the University of Michigan, Ann Arbor, and his B.S. from the National University of Singapore. His research interest broadly lies in the area of wireless mobile networks, cloud RANs, mobile computing and wireless sensing with a focus on cellular networks and systems. At NEC, his research lies along two directions: (a) 5G systems, specifically LTE, LTE unlicensed and massive MIMO; and (b) wireless sensing and IoT solutions. He has published in numerous conferences and journals such as ACM MobiCom, MobiHoc, IEEE INFOCOM, ICNP etc.

Elza Erkip, New York University

Wednesday, December 7, 2016 - 10:00am - 11:00am

CoRE Building Lecture Hall


2016 marks the hundredth birthday of Claude Shannon, the founder of information theory. Information theory laid out the foundations of modern communication systems; how will it impact 5G wireless networking? This talk addresses the question in the context of cooperative communications. During the past 45 years, information theory literature has provided a wide range of fundamental results establishing benefits of cooperation in various wireless scenarios. The impending 5G wireless revolution provides the perfect setting for reaping the potential gains of cooperation: Large number of antennas and wide bandwidth, as in millimeter wave systems, provide abundant degrees of freedom; cloud computing and cheap storage enable enhanced computing capabilities at the network edge; full-duplex radio designs allow nodes to transcend traditional duplexing limitations; and applications such as Internet of Things provide a natural setting for cooperative communication and compression. This talk provides a brief overview of the information theoretic foundations of cooperative communications along with a few examples of how even simple forms of cooperation could make big impact in 5G wireless networks.

Speaker Bio:

Dr. Elza Erkip is a Professor of Electrical and Computer Engineering with New York University Tandon School of Engineering. She is a Fellow of the IEEE, a member of the Science Academy Society of Turkey and is among the 2014 and 2015 Thomson Reuters Highly Cited Researchers. She received the NSF CAREER award in 2001 and the IEEE Communications Society WICE Outstanding Achievement Award in 2016. Her paper awards include the 2004 IEEE Communications Society Stephen O. Rice Paper Prize and the 2013 IEEE Communications Society Award for Advances in Communication. She has been a member of the Board of Governors of the IEEE Information Theory Society since 2012 where she is currently the Second Vice President. She was a Distinguished Lecturer of the IEEE Information Theory Society from 2013 to 2014.

Lei Ding, University of Oklahoma

Wednesday, November 30, 2016 - 10:00am - 11:00am

CoRE Building Lecture Hall

Title: Accessible Functional Neuroimaging Technologies for Understanding, Diagnosis, and Treatment Evaluation of Brain Disorders


Much of the recent progresses in neuroscience, concerning language, attention, memory, and many others that were intractable in the past, are derived from technological advancements in functional neuroimaging. These technologies are rapidly entering clinical practice and have dramatically advanced our capability in medical diagnosis and presurgical planning of various neurological diseases. Functional magnetic resonance imaging (fMRI) has excellent millimeters spatial resolution, while both electroencephalogram (EEG) and magnetoencephalogram (MEG) provides millisecond temporal resolution in researching human brain activity. Unfortunately, EEG/MEG suffer from low spatial resolutions and ambiguity in defining spatial origins of brain activity because of the volume conductor effect, while fMRI is fundamentally limited in studying the temporal aspect of brain activity at the neuronal time scale of milliseconds, which is the essence of brain function, due to its relatively slow acquisition speed and slow hemodynamic responses. We have developed a suite of innovative functional neuroimaging technologies to address these limitations in both EEG/MEG and fMRI, as well as to combine them together. In this talk, we will discuss functional neuroimaging technologies based on EEG and MEG developed in our lab for the understanding, diagnosis, and treatment evaluation of brain disorders. Compared with fMRI based functional neuroimaging technologies, they are more accessible, easy-to-use, cheap, and supporting large-scale longitudinal studies. We also demonstrate the theoretical merits of these technologies since they are from direct measurements of primary responses of neurons and reflect dynamics of orchestrated neuronal activations.


Dr. Lei Ding is a Presidential Professor of Biomedical Engineering, Electrical and Computer Engineering, and Neuroscience, and Director of Institute for Biomedical Engineering, Science, and Technology (IBEST) at the University of Oklahoma. He is also an Affiliated Associate Professor at the Laureate Institute for Brain Research, Tulsa, Oklahoma. He has made significant original contributions to research in functional neuroimaging, noninvasive neuromodulation, brain-computer interface, and imaging biomarkers for neurological and psychiatric disorders. He received B.E. degree (Highest Honors) from Zhejiang University, China (2000), and Ph.D. degree (Dissertation Fellow) from the University of Minnesota (2007), both in biomedical engineering.

Dr. Ding is the recipient of the National Science Foundation CAREER Award and the only recipient of the New Scientist Award from the State of Oklahoma in 2009. He is the recipient of Early Career Achievement Award of 2016 from IEEE Engineering in Medicine and Biology Society, the world largest biomedical engineering society. He has published over 80 peer-reviewed papers in areas of medical imaging and neural engineering. Dr. Ding is a member of the Institute of Electrical and Electronics Engineers (IEEE), IEEE Engineering in Medicine and Biology Society (EMBS), International Society for Bioelectromagnetism (ISBEM), Organization of Human Brain Mapping (OHBM), and Society for Neuroscience (SfN). He also serves as an Associate Editor for IEEE Transactions on Biomedical Engineering and IEEE Access, on editorial boards for several other biomedical journals, and as a reviewer for over 30 prestigious journals in the area of biomedical engineering. He has been a member of IEEE-EMBS Technical Committees on Biomedical Signal Processing and Biomedical Imaging/Image Processing and Associate Editor at EMBS Conference Editorial Board since 2008. He has served as Conference Program Chair/Co-Chair, Theme Chair/Co-Chair, Track Chair/Co-Chair or Session Chair/Co-Chair on EMBS Annual flagship conference and/or other special topic conferences at EMBS since 2007.

Prof. Richa Singh, IIIT
Prof. Mayank Vatsa, IIIT

Wednesday, November 16, 2016 - 10:00am - 11:00am

CoRE Building Lecture Hall


It is generally believed that face recognition by computers is a solved problem in many scenarios such as
user centric applications including face tagging in Facebook and screen/device unlocking in Android
and Windows-based systems. While significant advances have been made in the last two decades,
unconstrained face recognition is yet to benefit from these advances to be useful in real world
applications. Challenges such as newborn face recognition, forensic sketch to photo matching, matching
with disguise, aging, and plastic surgery variations are considered hard and require attention from the
research community. Advancements in pattern recognition and machine learning have helped face
recognition researchers in addressing challenges such as identity recognition in unconstrained
environment and very large scale identification. If we focus on different steps of a face recognition
pipeline, one of the important aspects is feature representation. While traditional approaches have
focused on handcrafted features such as Local Binary Pattern and Scale Invariant Feature Transform,
with the advent in computing technology, learning representations has attracted the attention of several
researchers worldwide. Research in representation learning focuses on understanding the data
characteristics and utilizing them for developing novel ways of extracting meaningful features that can
help in improving the system performance. In terms of face recognition, this improvement can be seen
as higher accuracies and improved user convenience. In recent years, deep learning-based methods have
gained a lot of attention from face recognition researchers and several deep learning based algorithms
have shown state-of-the-art accuracies. This presentation will provide an overview of progress made in
face recognition and different deep learning-based methods used for the same. We will also discuss
merits and limitations of available approaches and identify promising avenues of research in this rapidly
evolving field.

Short biography of the Speakers:

Richa Singh is an Associate Professor with the Indraprastha Institute of Information Technology, Delhi,
India, and a Visiting Professor with West Virginia University, USA. She has authored over 175
publications in refereed journals, book chapters, and conferences. Her areas of interest are biometrics,
pattern recognition, and machine learning. She is an Associate Editor of IEEE Access and an Editorial
Board Member of the Information Fusion (Elsevier), and the EURASIP Journal on Image and Video
Processing (Springer). She is a recipient of the Kusum and Mohandas Pai Faculty Research Fellowship
at IIIT Delhi, the FAST Award by DST, India, and several best paper and best poster awards in
international conferences. She is serving as the General Co-Chair of ISBA 2017 and the PC Co-Chair of
BTAS 2016. She has published several research papers in IEEE T-PAMI, IEEE T-IP, IEEE T-IFS and
other journals on deep learning for face recognition.

Mayank Vatsa is currently an Associate Professor with the IIIT-Delhi, India as well as Visiting and
Adjunct Faculty at West Virginia University, USA. He has authored about 200 publications in refereed
journals, book chapters, and conferences. His areas of interest are biometrics, image processing,
computer vision, machine learning, and information fusion. He is a recipient of the FAST Award by
DST, India, and several best paper, and best poster awards in international conferences. He is also the
Vice President (Publications) of IEEE Biometrics Council, an Associate Editor of the IEEE ACCESS
and an Area Editor of Information Fusion (Elsevier). He served as the PC Co-Chair of ICB 2013, IJCB
2014, and ISBA2017. Mayank has extensively worked on both theory and applications of deep learning
models, specially Autoencoders and Restricted Boltzmann Machines. He has proposed formulations of
supervised deep learning models and published papers in IEEE T-PAMI, IEEE T-IFS, Pattern
Recognition, and BTAS/IJCB on these topics.

Thomas A. Kennedy, Chair and CEO, Raytheon Company

Thursday, November 10, 2016 - 3:00pm - 4:00pm

Fiber Optics Auditorium

V. John Mathews, Oregon State University

Wednesday, November 9, 2016 - 10:00am - 11:00am

CoRE Building Lecture Hall


Recent technological innovations such as functional neural stimulation (FNS) offer considerable benefits to paralyzed individuals. FNS can produce movement in paralyzed muscles by the application of electrical stimuli to the nerves innervating the muscles. The first part of this talk will describe how smooth muscle movements can be evoked using electrode arrays inserted into the motor nerves of the peripheral nervous system. We will review approaches for designing asynchronously interleaved stimulation signals applied via individual electrodes in the arrays to evoke smooth, fatigue-resistant force that closely resembles normal motor function. The second part of this talk will describe efforts to decode human intent from neural signals. The decoded information can then be used to control the muscles in tasks involving restoration of motor skills or to prosthetic devices to perform desired tasks. Results of experiments involving human subjects will be described and discussed. The talk will conclude with a discussion of some of the current research challenges in this area.


Dr. V. John Mathews is a Professor and Head of the School of Electrical Engineering and Computer Science at the Oregon State University. His research interests are in nonlinear and adaptive signal processing and application of signal processing techniques in audio and communication systems, biomedical engineering, and structural health management. Previously he was with the University of Utah from 1985 till 2015. He chaired the department of Electrical and Computer Engineering at the University of Utah during 1999-2003. Dr. Mathews is a Fellow of IEEE. He served as the Vice President (Finance) of the IEEE Signal Processing Society during 2003-2005 and the Vice President (Conferences) of the Society during 2009-2011. He is a past associate editor of the IEEE Transactions on Signal Processing, and the IEEE Signal Processing Letters and the IEEE Journal of Selected Topics in Signal Processing and served on the editorial board of the IEEE Signal Processing Magazine. He was a recipient of the Meritorious Service Award from the IEEE Signal Processing Society in 2014, the 2008-09 Distinguished Alumnus Award from the National Institute of Technology, Tiruchirappalli, India, and the Utah Engineers Council's Engineer of the Year Award in 2011. He was a Distinguished Lecturer of the IEEE Signal Processing Society for 2013 and 2014.

Kaushik Sengupta, Princeton University

Wednesday, November 2, 2016 - 10:00am - 11:00am

CoRE Building Lecture Hall

Abstract: Integrated circuits now rely on transistors which are a few atoms in channel length integrated in a substrate which supports a billion of them interconnected in a multi-layer, complex metal interconnect mesh. While this has created fundamental challenges or ‘walls’ in processing scalability, it has also opened up new opportunities across the electromagnetic spectrum from DC-RF-optical frequencies that cut across traditional research boundaries. As an example, the enormous signal processing capabilities when combined with RF had led to new communication architectures with properties such as reconfigurability, concurrency, cognition and multi-spectral capability, all of which are critical for efficiently using the EM spectrum for the next-generation wireless infrastructure. Secondly, while scaling has pushed up operable frequencies into the higher mmWaveregion, the chip dimension also has become several times larger than the operating wavelengths. This allows new scattering and radiating properties where reconfigurable 2D THz radiating surfaces can be synthesized, manipulated and sensed at sub-wavelength scales for new forms of reconfigurable THz systems on chip. Thirdly, the sub-100 nm lithography of interconnect layers in silicon ICs allows for integrated, complex, sub-wavelength metal-optical structures with multi-functional properties that can miniaturize traditional, bulky optical instrumentations into chip-scale optical sensors. In this talk, I will give examples across the spectrum from mm-Wave-THz-optics on reconfigurable mm-Wave transmitters exploiting multi-port network synthesis approaches, THz spectrum sensing using near-field EM scattering, and integrated fluorescence-based biosensors with nanoplasmonic waveguides in CMOS for massively multiplexed biomolecular assays on chip. Harnessing such a spectral expanse in one integrated platform with techniques combining circuits, electromagnetics, optics and signal processing can lead to new capabilities and open untapped opportunities across a wide range of applications in communication, sensing and imaging.

Bio: Dr. Kaushik Sengupta received the B.Tech. and M.Tech. degrees in electronics and electrical communication engineering from the Indian Institute of Technology (IIT), Kharagpur, India, both in 2007, and the M.S. and Ph.D. degrees in electrical engineering from the California Institute of Technology, Pasadena, CA, USA, in 2008 and 2012, respectively. In February 2013, he joined the faculty of the Department of Electrical Engineering, Princeton University, Princeton, NJ, USA. He was the recipient of the IBM Ph.D. fellowship (2011), the IEEE Solid-State Circuits Society Pre-doctoral Achievement Award (2012), the IEEE Microwave Theory and Techniques Graduate Fellowship (2012), and the Analog Devices Outstanding Student Designer Award (2011). He was the recipient of the Charles Wilts Prize in 2013 from Electrical Engineering, Caltech for outstanding independent research in electrical engineering leading to a PhD. He was also the recipient of the Prime Minister Gold Medal Award of IIT (2007), the Caltech Institute Fellowship, He was selected in `Princeton Engineering Commendation List for Outstanding Teaching' in 2014. He serves on the Technical Program Committee of IEEE Custom Integrated Circuits conference and European Solid-state Circuits Conference. He was the co-recipient of the IEEE RFIC Symposium Best Student Paper Award in 2012 and 2015 Microwave Prize from IEEE Microwave Theory and Techniques Society.

Dr. Alexander Leonessa, Virginia Tech

Wednesday, October 26, 2016 - 10:00am - 11:00am

CoRE Building Lecture Hall

EEG-Based Control of Working Memory Maintenance Using Closed-Loop Binaural Stimulation

by Alexander Leonessa

The brain is a highly complex network of nonlinear systems with internal dynamic states that are not easily quantified. As a result, it is essential to understand the properties of the connectivity network linking disparate parts of the brain used in complex cognitive processes, such as working memory. Working memory is the system in control of temporary retention and online organization of thoughts for successful goal directed behavior. Individuals exhibit a typically small capacity limit on the number of items that can be simultaneously retained in working memory. To modify network connections and thereby augment working memory capacity, researchers have targeted brain areas using a variety of noninvasive stimulation interventions; methods of stimulation include electrical signals, magnetic fields, and ultrasounds. However, few existing methods take advantage of the brain's own structure to actively generate and entrain internal oscillatory modulations in locations deep within the auditory pathways. One technique is known as binaural beats, which arises from the brain's interpretation of two pure tones, with a small frequency mismatch, delivered independently to each ear. The mismatch between these tones is perceived as a so-called beat frequency. The use of binaural beats to entrain certain brain structures has been explored and results suggest that this safe and accessible stimulation method can be used to modulate behavioral performance and cortical connectivity in both verbal and visuospatial working memory tasks.

Currently, all binaural therapeutic stimulation systems are open-loop “one-size-fits-all” approaches. However, these methods can prove not as effective because each person's brain responds slightly differently to exogenous stimuli. Therefore, the driving motivation for developing a closed-loop stimulation system is to help populations with large individual variability. One such example is persons with mild cognitive impairment (MCI). MCI causes cognitive impairments beyond those expected based on age and the ability to perform complex tasks are impaired. Therefore, applying a closed-loop binaural beat control system to increase the cognitive load level to people with MCI could potentially reduce their rate of cognitive decline and maintain their quality of life. In this talk I will first present preliminary data based on open-loop based binaural stimulation. In particular, based on graphical network analyses, the cortical activity during 15Hz binaural beats produced networks characteristic of high information transfer with consistent connection strengths throughout working memory tasks. Finally, I will describe our future plans for implementing “The Virtual Brain” simulation environment as well as actual patients to study the effectiveness of several closed-loop control strategies to effectively control brain networks hence influencing cognitive abilities.

Alexander Leonessa’s Biosketch

Dr. Alexander Leonessa obtained a Doctoral degree in Aerospace Engineering at GeorgiaTech in December 1999. His research focused on nonlinear robust control techniques for general nonlinear systems. His appointment as a faculty member at Virginia Tech started in December 2007, after two previous similar appointments at Florida Atlantic University and the University of Central Florida. His research and contribution include (i) control theory with application to autonomous vehicles guidance and navigation, (ii) nonlinear system identification with application to health monitoring, (iii) real-time embedded control with application to system design of robotic systems, and (iv) functional electrical stimulation of muscles for rehabilitation of stroke survivors and patients with spinal cord injuries. In particular, the dominant idea in his research effort is that most real-world physical systems are too complex to accurately model, hence model uncertainties must be accounted for in the control system design process using some kind of self-learning procedure. Dr. Leonessa has been involved in these areas of research for more than 15 years during which he has published more than 60 papers (all peer reviewed). In 2014-2016 he completed a two-year rotation at the National Science Foundation, where he was supervising the General and Age Related Disability Engineering (GARDE) program as well as participating to the Major Research Instrumentation program, the National Robotic Initiative, the Partnership for Innovation program, and the Integrative Strategies for Understanding Neural and Cognitive Systems program.

Dr. Connie Wu, Rutgers University Libraries

Wednesday, October 19, 2016 - 10:00am - 11:00am

CoRE Building Lecture Hall

Universities play an important role in advancing the frontiers of science and technology. Obtaining a basic understanding of IP is an important part of engineering education. Today’s engineers and scientists should not only be trained with traditional technical skills and scientific research methodologies, but they also need a basic understanding of the various forms of intellectual property, especially patent knowledge.

This talk will introduce students to various forms of IP with an emphasis on patents. Patent database search, patent application steps and invention process will be included in the talk. Students will benefit from this knowledge and will be better prepared as they enter the workforce upon graduation.

Dr. Connie Wu is a tenured faculty member and Engineering and Patent Information Specialist at the Rutgers University. Before her library and information sciences career, Connie was a patent examiner working at the Patent Office of China and an Engineer and Project Manager at Research Department, Beijing Research Institute of Postal Technology. Her major research is on engineering and patent information dissemination and analysis. She has received numerous honors and grants, including the Whitney-Carnegie Award; Executive Committee Resolution for leadership from the Patent and Trademark Depository Library Association; Outstanding Service Award from Chinese American Academic and Professional Society; Best Research Award of New Jersey Library Association; Certificate of Appreciation for Services Rendered from National Society of Inventors. Her research has been supported by grants from the Engineering Information Foundation, United Nations, the China National Foreign Expert Bureau. She has lectured frequently at conferences and universities in the US and abroad on intellectual property and other topics.

Dr. Armando Solar Lezma, Massachusetts Institute of Technology

Wednesday, October 5, 2016 - 10:00am - 11:00am

CoRE Lecture Hall

Title: Making Synthesis Real


Recent years have seen dramatic improvements in our ability to synthesize non-trivial functions. These advances have opened the door to a range of new applications beyond automated programming, ranging from program optimization to automated grading.

This talk will describe some of the algorithms behind these recent advances, as well as some of the applications that have been enabled by them. I will focus on how automated solvers for SAT and SMT problems have enabled advances in our ability to search for programs that satisfy a specification. While the core algorithms can only scale to relatively small programs, they can be combined with more traditional compilation techniques to produce code that is more efficient and more complex than what a human can produce by hand. They can also enable applications beyond automated programming, such as automated tutoring, where the goal is to provide personalized feedback on programming assignments without the need for human intervention.


Dr. Armando Solar-Lezama is an Associate Professor at MIT where he leads the Computer Aided Programming Group. His research interests include software synthesis and its applications, as well as high-performance computing, information flow security and probabilistic programming

Dr. Jeffrey Lang, Massachusetts Institute of Technology

Wednesday, April 27, 2016 - 10:00am - 11:00am

CoRE Building Lecture Hall

Dr. Jeffrey H. Lang
Vitesse Professor of Electrical Engineering
EECS Department, MIT

Electromechanical Power MEMS/NEMS


MEMS sensors such as accelerometers, gyroscopes, microphones, pressure sensors, and biochemical sensors have transformed automotive, medical and consumer industries among others. In contrast, with the exception of projection displays, MEMS actuators have not yet been similarly transformative. The objective of this talk is to review the development and physical limits of several electromechanical MEMS/NEMS actuators with an eye to understanding where they might become transformative. Micro-scale motors and generators, micro-scale power relays and nano-scale signal relays serve as actuator examples. Electric and magnetic micro-scale motors and generators have achieved power densities similar to those of macro-scale machines. MEMS power relays have demonstrated kilo-volt-amp blocking-voltage-by-conduction-current products, and NEMS relays are beginning to operate at sufficiently low voltages to challenge low-power transistors. These achievements will be reviewed, as well as the lessons learned from their development, ending with conclusions concerning the future application of MEMS/NEMS actuators.


Jeffrey H. Lang received his SB (1975), SM (1977) and PhD (1980) degrees from the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. He joined the faculty of MIT in 1980 where he is now the Vitesse Professor of Electrical Engineering. He served as the Associate Director of the MIT Laboratory for Electromagnetic and Electronic Systems from 1991 to 2003, and has served as an Associate Director of the MIT Microsystems Technology Laboratories since 2012.

Professor Lang's research and teaching interests focus on the analysis, design and control of electromechanical systems with an emphasis on: rotating machinery; micro/nano-scale (MEMS/NEMS) sensors, actuators and energy converters; flexible structures; and the dual use of electromechanical actuators as motion and force sensors. He has written over 280 papers and holds 12 patents in the areas of electromechanics, MEMS/NEMS, power electronics and applied control. He is a coauthor of Foundations of Analog and Digital Electronic Circuits published by Morgan Kaufman, and the editor of, and a contributor to, Multi-Wafer Rotating MEMS Machines: Turbines Generators and Engines published by Springer. Professor Lang is a Fellow of the IEEE, and a former Hertz Foundation Fellow. He served as an Associate Editor of Sensors and Actuators between 1991 and 1994. He has also served as the General Co-Chair and Technical Co-Chair of the 1992 and 1993 IEEE MEMS Workshops, respectively, and the General Co-Chair of the 2013 PowerMEMS Conference.

Dr. Tulay Adali, University of Maryland, Baltimore County

Wednesday, April 20, 2016 - 2:00pm - 3:00pm

CoRE Building Lecture Hall

Single and Multi-set Source Separation: Why and How to Account for Multiple Types of Statistical Diversity

Data-driven methods are based on a simple generative model and hence can minimize the underlying assumptions on the data. They have emerged as promising alternatives to the traditional model-based approaches in applications where the unknown dynamics are hard to characterize. This has been the main reason for the growing importance of data-driven methods, and in particular of independent component analysis (ICA) as it provides useful decompositions with a simple generative model and using only the assumption of statistical independence. A recent extension of ICA, independent vector analysis (IVA), generalizes ICA to multiple datasets by exploiting the statistical dependence across the datasets, and provides an attractive solution to fusion of data from multiple datasets along with ICA.

Both ICA and IVA can be achieved using different types of diversity---different statistical properties---and, as demonstrated in this talk, can be posed to simultaneously account for multiple types of diversity such as higher-order-statistics, sample dependence, non-circularity, and nonstationarity. Employing multiple types of diversity enables maximal use of all available information, and with the addition of each new diversity type, identification of a broader class of signals become possible, which, in the case of IVA, includes sources that are independent and identically distributed Gaussians.

This talk reviews the fundamentals and properties of ICA and IVA when multiple types of diversity are taken into account, and introduces two powerful models for fusion of multiple datasets. Examples are presented in medical imaging and in video surveillance to demonstrate the importance of diversity from a practical point of view as well.


Tülay Adali received the Ph.D. degree in Electrical Engineering from North Carolina State University, Raleigh, NC, USA, in 1992 and joined the faculty at the University of Maryland Baltimore County (UMBC), Baltimore, MD, the same year. She is currently a Distinguished University Professor in the Department of Computer Science and Electrical Engineering at UMBC.

She has been very active in conference and workshop organizations. She was the general or technical co-chair of the IEEE Machine Learning for Signal Processing (MLSP) and Neural Networks for Signal Processing Workshops 2001-2008, and helped organize a number of conferences including the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). She has served or currently serving on numerous editorial boards and technical committees of the IEEE Signal Processing Society. She was the chair of the technical committee on MLSP, 2003-2005 and 2011-2013.

Prof. Adali is a Fellow of the IEEE and the AIMBE, a Fulbright Scholar, recipient of a 2010 IEEE Signal Processing Society Best Paper Award, 2013 University System of Maryland Regents' Award for Research, and an NSF CAREER Award. She was an IEEE Signal Processing Society Distinguished Lecturer for 2012 and 2013.
Her research interests are in the areas of statistical signal processing, machine learning for signal processing, and biomedical data analysis.

Dr. Mingyan Liu, University of Michigan

Wednesday, March 30, 2016 - 10:00am - 11:00am

CoRE Building Lecture Hall


Forecasting Cybersecurity Incidents and Its Role in Designing Incentive Mechanisms


In this talk I will present a number of predictive analytics studies we performed over the past few years aimed at characterizing the extent to which cyber security incidents can be predicted based on externally observable properties of an entity's network. While the procedure follows the standard framework of supervised learning, significant challenges arose in (1) determining what types of data to collect, (2) how to clean and align the data in both space and time, and (3) how to deal with various deficiencies in the data. I will first describe the use of host malicious activity data (including spam, phishing, and active scanning) combined with network configuration data to obtain incident prediction at an organizational level. I will then describe the additional use of business details about an organization to obtain more fine-grained prediction, which looks at not just the overall risk of an incident, but the types of incidents it is particularly susceptible to. I will end the talk by describing how our ability to make predictions, or more generally, our ability to quantify at a global level the security postures of organizations, may be viewed as creating a form of "public monitoring", which can be crucial in designing mechanisms that rely on inter-temporal incentives to induce socially desirable behaviors, from security practice to security investment to security information sharing.

This is joint work with my current students Parinaz Naghizadeh and Armin Sarabi, my former student Yang Liu, as well as Prof. Michael Bailey from UIUC.

Dr. William Sanders, University of Illinois Urbana-Champaign

Wednesday, March 9, 2016 - 10:00am - 11:00am

CoRE Building Lecture Hall

Title: Challenges and Approaches for a Trustworthy Power Grid Cyber Infrastructure


The vision for a modernized "Smart Grid" involves the use of an advanced computing, communication and control cyber infrastructure for enhancing current grid operations by enabling timely interactions among a range of entities. The coupling between the power grid and its cyber infrastructure is inherent, and the extent to which the Smart Grid vision can be achieved depends upon the functionality and robustness of the cyber infrastructure. This talk describes some of the research results from the DOE- and DHS-funded Trustworthy Cyber Infrastructure for the Power Grid (TCIPG) Center which is aimed at ensuring that the power grid cyber infrastructure is protected both from accidental failures and malicious attacks from adversaries ranging from casual hackers to nation states. The goal of TCIPG was to provide resilience in the nation¹s electric grid cyber infrastructure such that it continues to deliver electricity and maintain critical operations even in the presence of cyber attacks. Achieving this goal will involve the extension, integration, design, and development of IT technologies imbibed with key properties of real-time availability, integrity, authentication and confidentiality.


William H. Sanders is a Donald Biggar Willett Professor of Engineering and the Head of the Department of Electrical and Computer Engineering ( at the University of Illinois at Urbana-Champaign. He is a professor in the Department of Electrical and Computer Engineering and Affiliate Professor in the Department of Computer Science. He is a Fellow of the IEEE, the ACM, and the AAAS; a past Chair of the IEEE Technical Committee on Fault Tolerant Computing; and past Vice-Chair of the IFIP Working Group 10.4 on Dependable Computing. He was the founding Director of the Information Trust Institute ( at Illinois (2004-2011), and served as Director of the Coordinated Science Laboratory ( at Illinois from 2010 to 2014. Sanders's research interests include secure and dependable computing and security and dependability metrics and evaluation, with a focus on critical infrastructures. He has published more than 200 technical papers in those areas. He was the Director and PI of the DOE/DHS Trustworthy Cyber Infrastructure for the Power Grid (TCIPG) Center (

He is also co-developer of three tools for assessing computer-based systems: METASAN, UltraSAN, and Möbius. Möbius and UltraSAN have been distributed widely to industry and academia; more than 1,400 licenses for the tools have been issued to universities, companies, and NASA for evaluating the performance, dependability, and security of a variety of systems. He is also a co-developer of the NP-View tool ( for assessing the security of networked systems.

Dr. Krishnendu Chakrabarty, Duke University

Wednesday, February 17, 2016 - 1:00pm - 2:00pm

CoRE Building Lecture Hall

Please note the time of this colloquium is from 1:00 PM to 2:00 PM in the CoRE Lecture Hall

Digital Microfluidic Biochips: From Manipulating Droplets to Quantitative Gene-Expression Analysis

Abstract: Advances in microfluidics have led to the emergence of biochips for automating laboratory procedures in molecular biology. These devices enable the precise control of nanoliter volumes of biochemical samples and reagents. As a result, non-traditional biomedical applications and markets (e.g., high-throughout DNA sequencing, portable and point-of-care clinical diagnostics, protein crystallization for drug discovery), and fundamentally new uses are opening up for ICs and systems. This lecture will first introduce electrowetting-based digital microfludic biochips and provide an overview of market drivers such as immunoassays and DNA sequencing. The audience will next
learn about design automation and reconfiguration aspects of microfluidic biochips. Synthesis tools will be described to map assay protocols from the lab bench to a droplet-based microfluidic platform and generate an optimized schedule of bioassay operations, the binding of assay operations to functional units, and the layout and droplet-flow paths for the biochip. The role of the digital microfluidic platform as a “programmable and reconfigurable processor” for biochemical applications will be highlighted. The speaker will also describe dynamic adaptation of bioassays through cyberphysical system integration and sensor-driven on-chip error recovery. Finally, the speaker will highlight recent advances in utilizing cyberphysical integration for quantitative gene-expression analysis. This framework is based on a real-time
resource-allocation algorithm that responds promptly to decisions about the protocol flow received from a firmware layer. Results will be presented on how this adaptive framework efficiently utilizes on-chip resources to reduce time-to-result without sacrificing the chip’s lifetime.

Bio: Dr. Krishnendu Chakrabarty received the B. Tech. degree from the Indian Institute of Technology, Kharagpur, in 1990, and the M.S.E. and Ph.D. degrees from the University of Michigan, Ann Arbor, in 1992 and 1995, respectively. He is now the William H. Younger Distinguished Professor of Engineering in the Department of Electrical and Computer Engineering and Professor of Computer Science at Duke University. He also serves as Director of Graduate Studies for Electrical and Computer Engineering. Prof. Chakrabarty is a recipient of the National Science Foundation Early Faculty (CAREER) award, the Office of Naval Research Young Investigator award, the Humboldt Research Award from the Alexander von Humboldt Foundation, Germany, the IEEE Transactions on CAD Donald O. Pederson Best Paper award (2015), and 11 best paper awards at major IEEE conferences. He is also a recipient of the IEEE Computer Society Technical Achievement Award (2015) and the Distinguished Alumnus Award from the Indian Institute of Technology, Kharagpur (2014). He is a Research Ambassador of the University of Bremen (Germany).

Prof. Chakrabarty’s current research projects include: testing and design-for-testability of integrated circuits; digital microfluidics, biochips, and cyberphysical systems; optimization of enterprise systems and smart manufacturing. He has authored 17 books on these topics, published over 550 papers in journals and refereed conference proceedings, and given over 250 invited, keynote, and plenary talks. He has also presented 40 tutorials at major international conferences.

Prof. Chakrabarty is a Fellow of ACM, a Fellow of IEEE, and a Golden Core Member of the IEEE Computer Society. He holds seven US patents, with several patents pending. He was a 2009 Invitational Fellow of the Japan Society for the Promotion of Science (JSPS). He is a recipient of the 2008 Duke University Graduate School Dean’s Award for excellence in mentoring, and the 2010 Capers and Marion McDonald Award for Excellence in Mentoring and Advising, Pratt School of Engineering, Duke University. He has served as a Distinguished Visitor of the IEEE Computer Society (2005-2007, 2010-2012), and as a Distinguished Lecturer of the IEEE Circuits and Systems Society (2006-2007, 2012-2013). Currently he serves as an ACM Distinguished Speaker.

Prof. Chakrabarty served as the Editor-in-Chief of IEEE Design & Test of Computers during 2010-2012 and ACM Journal on Emerging Technologies in Computing Systems during 2010-2015. Currently he serves as the Editor-in-Chief of IEEE Transactions on VLSI Systems. He is also an Associate Editor of IEEE Transactions on Computers, IEEE Transactions on Biomedical Circuits and Systems, IEEE Transactions on Multiscale Computing Systems, and ACM Transactions on Design Automation of Electronic Systems. He serves as an Editor of the Journal of Electronic Testing: Theory and Applications (JETTA). In the recent past, he has served as Associate Editor of IEEE Transactions on VLSI Systems (2005-2009), IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2001-2013), IEEE Transactions on Circuits and Systems I (2005-2006), and IEEE Transactions on Circuits and Systems II (2010-2013).

Event Date and Time:
Feb 17 2016 - 1:00pm - 2:00pm
CoRE Lecture Hall

Prof. Anand Sarwate, Rutgers, Department of Electrical & Computer Engineering

Wednesday, February 10, 2016 - 10:00am - 11:00am

CoRE Lecture Hall

Making the Most of Your Time in Graduate School at Rutgers ECE


Starting graduate school can be a big change from undergraduate studies or industry work: the rules and expectations are different, and it can be challenging to navigate a new system and environment.

Grad school is also full of opportunities to learn about new fields, get involved in research, and prepare yourself for a professional career as an engineer. To take advantage of these opportunities, you have to know your own objectives are, the rules of the game, and potential challenges you might face. In this talk I'll try to demystify some of this process, provide some insight from others on what to do, and give some concrete tips on how to proceed right now to make the most of your time here.


​Anand D. Sarwate joined as an Assistant Professor in the Department of Electrical and Computer Engineering at Rutgers, the State University of New Jersey in 2014. He received B.S. degrees in Electrical Engineering and Mathematics from MIT in 2002, an M.S. in Electrical Engineering from UC Berkeley in 2005 and a PhD in Electrical Engineering from UC Berkeley in 2008. From 2008-2011 he was a postdoctoral researcher at the Information Theory and Applications Center at UC San Diego and from 2011-2013 he was a Research Assistant Professor at the Toyota Technological Institute at Chicago, a philanthropically endowed academic computer science institute located on the University of Chicago campus. Prof. Sarwate received the NSF CAREER award in 2015. His interests are in information theory, machine learning, and signal processing, with applications to distributed systems, privacy and security, and biomedical research.

Presentation slides of Prof. Sarwate's presentation in PDF format

Mei Ling Lo,
Rutgers University Libraries

Wednesday, January 27, 2016 - 10:00am - 11:00am

CoRE Lecture Hall

Abstract: This presentation will introduce you to library databases that you can use for finding articles related to your topic. Learn how to use advanced features of the databases to set up automated alerts and keep abreast of current developments in your area. In addition, this presentation will demystify various ways to measure your research impact including citation analysis, the H-Index, and Journal Citation Reports. By the end of the presentation, you will have a better understanding of the tricks that some authors use to boost their research impact.

Bio: Mei Ling Lo is the Mathematics/Computer Science Librarian at Rutgers Libraries. She is also the interim liaison for Business School at New Brunswick/Piscataway Campus. She is responsible for collection development. She also provides library instructions to Rutgers students. Mei Ling is particularly interested in using technologies to help students achieve greater learning outcomes and was the recipient of the New Jersey Libraries Association Technology Innovation Award. She received her M.L.I.S. from Columbia University in the City of New York and was a Science Librarian at Columbia University before joining Rutgers. If you have any questions about the libraries, please feel free to contact her directly at mlo [at] rutgers [dot] edu.

Presentation slides of Mei Ling Lo's presentation in PDF format

Presentation slides of Mei Ling Lo's presentation in Powerpoint format

Wednesday, December 9, 2015 - 10:00am - 11:00am

Core Building Lecture Hall

Ram Reddy, CommuniClique, Inc.

Wednesday, November 18, 2015 - 10:00am - 11:00am

CoRE Building Lecture Hall

Dmitri Chklovskii, The Simons Foundation

Wednesday, November 11, 2015 - 10:00am - 11:00am

CoRE Lecture Hal

Title:   Similarity matching: a new principle of neural computation


Inspired by experimental neuroscience results we developed a family of online algorithms that reduce dimensionality, cluster and discover features in streaming data. The novelty of our approach is in starting with similarity matching objective functions used offline in Multidimensional Scaling and Symmetric Nonnegative Matrix Factorization. We derived online distributed algorithms that can be implemented by biological neural networks resembling brain circuits. Such algorithms may also be used for Big Data applications.


Dmitri "Mitya" Chklovskii is Group Leader for Neuroscience at the Simons Center for Data Analysis in New York City. His goal is to understand the brain by analyzing experimental data and constructing the theory of neural computation. He received a PhD in Theoretical Physics from MIT and was a Junior Fellow at the Harvard Society of Fellows. He switched from physics to neuroscience at the Salk Institute and founded the first theoretical neuroscience group at Cold Spring Harbor Laboratory in 1999, where he was an Assistant and then Associate Professor. From 2007 to 2014 he was a Group Leader at Janelia Farm where he led a team that assembled the largest-ever connectome.

Venu Veeravalli, University of Illinois Urbana-Champaign

Wednesday, November 4, 2015 - 10:00am - 11:00am

Core Building Lecture Hall

Title: Quickest Detection and Isolation of Line Outages in Power Systems

Abstract: The problem of detecting abrupt changes in stochastic systems and time series, often referred to as the quickest change detection (QCD) problem, arises in various branches of science and engineering. It is assumed that the observations undergo a change in distribution in response to a change or disruption in the environment or, more generally, to changes in certain patterns. The observations are obtained sequentially and, as long as their behavior is consistent with the normal state, the process is allowed to continue. If the state changes, then it is of interest to detect the change as soon as possible, subject to false alarm constraints, and take any necessary action in response to the change. In the first part of this talk, an up-to-date overview of the results on the QCD problem will be provided, including some recent results on data-efficient QCD. A number of applications of QCD will be discussed. In the second part of the talk, the focus will be on the problem of detecting and isolating line outages in power systems using phasor measurement unit (PMU) measurements taken at the buses. It is shown that QCD based algorithms are tailor-made for this problem and significantly outperform existing methods.

Bio: Prof. Veeravalli received the Ph.D. degree in Electrical Engineering from the University of Illinois at Urbana-Champaign in 1992, the M.S. degree from Carnegie-Mellon University in 1987, and the B.Tech degree from Indian Institute of Technology, Bombay (Silver Medal Honors) in 1985. He is currently a Professor in the department of Electrical and Computer Engineering (ECE), the Coordinated Science Laboratory (CSL) and the Information Trust Institute (ITI) at the University of Illinois at Urbana-Champaign. He was on the faculty of the School of ECE at Cornell University before he joined Illinois in 2000. He served as a program director for communications research at the U.S. National Science Foundation in Arlington, VA during 2003-2005. His research interests span the theoretical areas of detection and estimation, information theory, statistical learning, and stochastic control, with applications to wireless communication systems and networks, sensor networks, cyberphysical systems, big data, and genomics. He is a Fellow of the IEEE, and a recipient of the 1996 IEEE Browder J. Thompson Best Paper Award and the U.S. Presidential Early Career Award for Scientists and Engineers (PECASE). He served as a distinguished lecturer for the IEEE Signal Processing Society during 2010-2011.

Tara Alvarez, New Jersey Institute of Technology

Wednesday, October 14, 2015 - 10:00am - 11:00am

CoRE Building Lecture Hall

Title:   The Underlying Mechanism of Vision Therapy revealed using Functional Imaging


Convergence insufficiency (CI), a prevalent binocular vision disorder in adults and children, is characterized by greater exophoria at near than at distance, reduced fusional convergence amplitude and receded near point of
convergence. CI is associated with symptoms including double/blurred vision, eyestrain, and headaches when engaged in reading or other near work. Dr. Alvarez and colleagues published the first data of functional activity using functional MRI (fMRI) with convergence eye movements correlated to vision function in CI patients before and after Office-Based Vergence and Accommodative Therapy with home reinforcement (OBVAT). The recent NEI/NIH multi-center randomized clinical trial, the Convergence Insufficiency Treatment Trial (CITT), demonstrated the effectiveness of OBVAT for CI, reporting 73% of patients have sustained improvements of vision function and symptoms. Our quantitative methods integrated with established CITT standards address important questions about the underlying neural substrates and changes in convergence presumed to be evoked by OBVAT. We combine objective eye movement recordings and fMRI, to take a first critical step to answer what neural substrates change within the visual system as the near point of convergence and convergence amplitudes improve with therapy. The relationship between convergence eye movements and fMRI will be discussed. Measurements from the CI patients were obtained before and after 18 hours of vision therapy. Neural substrates were stimulated using an fMRI block design composed of sustained fixation compared to vergence eye movements. Individual subject reference vectors were computed using a data-driven independent component analysis (ICA) of a group of time series. After vision therapy, the blood oxygenation level dependent (BOLD) percent signal change increased in the frontal eye fields (FEF), posterior parietal cortex (PPC) and cerebellar vermis (CV). Second, the task induced functional connectivity improved in the FEF, PPC, and CV. Last, the BOLD percent signal change within FEF and CV significantly correlated to the clinically developed symptom survey.


Tara Alvarez is Professor of Biomedical Engineering at NJIT. After her Ph.D. (BME, Rutgers) and research at Bell Labs, she helped found NJIT’s BME Department in 2001. Her laboratory seeks to understand fundamental mechanisms of oculomotor learning. She actively studies how clinical vision therapy leads to sustained reduction in visual dysfunction. (5% of children and 40-50% of traumatic brain injured patients suffer oculomotor dysfunction.) Dr. Alvarez is known for demonstrating how vision therapy alters neural oculomotor control using combined measurements of functional imaging (fMRI) and eye movements. This knowledge improves therapeutic efficacy while decreasing cost – especially for children and those with brain injury. Her work is funded by NIH, where her most recent grant placed in the top 5%, NSF and Essilor International. She has 125 peer reviewed publications and 4 patents. She has been recognized by numerous awards: NSF CAREER (2005), Best Scientific Achievement (NORA, 2008), Outstanding Woman of Science (NJABR, 2008), NJIT Excellence in Education (2013), NJIT Excellence in Research (2015), Thomas Edison Award (NJ R&D Council, 2015). She serves on the editorial board of Journal of Eye Movement Research and actively reviews for 7 other journals.

Shoaib Kamil, Adobe Research/MIT

Wednesday, October 7, 2015 - 10:00am - 11:00am

CoRE Lecture Hall

Title: Computer-Aided Programming: Productivity and High Performance

Abstract: Due to the end of clock speed scaling, parallelism has become the only path forward for maintaining performance gains from Moore's Law. At the same time, the proliferation of accelerators, hierarchical architectures, GPUs, and mobile devices has led to a multiplicative effect on the amount of time required for programming these systems to obtain high performance. Furthermore, maintainability has suffered since the usual optimization techniques require hand-rewriting code in ways that mix semantic meaning with optimizations. This talk presents research towards a path forward using domain-specific embedded languages and compilers, program synthesis, and automated optimization/learning techniques together to build new systems that enable programmers to express high-level intent and obtain portable high performance, while increasing both productivity and maintainability. With these new programming systems, we can harness the power of computers to make high performance programming simpler and easier.

Bio: Shoaib Kamil's research spans the areas of high performance programming and computing, domain specific languages and compilers, and program synthesis. Most recently, he was a research scientist at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology, working with Profs. Saman Amarasinghe and Armando Solar-Lezama. Prior to MIT, he obtained his PhD from the University of California at Berkeley where his research became the centerpiece of two large multi-PI projects, including the Parallel Computing Laboratory, funded by Intel and Microsoft. He has also worked at Lawrence Berkeley National Laboratory on large-scale codes running on supercomputers for climate simulation, among other applications.

Prof. Janne Lindqvist, Rutgers, Department of Electrical & Computer Engineering

Wednesday, September 16, 2015 - 10:00am - 11:00am

CoRE Lecture Hall

Title: "Science in/for/of Smartphone Authentication?"


Smartphone authentication has received considerable attention of the research community for the past several years. Every year, in diverse set of top conferences, such as, CHI, MobiSyS, MobiCom, UbiComp, USENIX Security, CCS, NDSS, you can find some new alternative authentication mechanism proposal. This is not surprising given how important smartphones have become for people's daily lives.

Smartphone authentication is also of particular interest because there have been also deployments by the industry beyond the usual PINs and passwords. Two prominent examples include the iOS's fingerprint authentication mechanism TouchID and Android's 3x3 grid-based graphical password.

But what is good? What should we actually use? How should we answer this question? In this talk, we'll discuss our group's answers and overview our work towards usable and secure smartphone authentication.


Janne Lindqvist is an assistant professor of electrical and computer engineering and a member of WINLAB at Rutgers University, where he directs the Rutgers Human-Computer Interaction Group. From 2011-2013, Janne was an assistant research professor of WINLAB/ECE at Rutgers. Prior to Rutgers, Janne was a post-doc with the Human-Computer Interaction Institute at Carnegie Mellon University's School of Computer Science.

Janne received his M.Sc. degree in 2005, and D.Sc. degree in 2009, both in Computer Science and Engineering from Helsinki University of Technology, Finland.

He works at the intersection of security engineering, human-computer interaction and mobile computing. Before joining academia, Janne co-founded a wireless networks company, Radionet, which was represented in 24 countries before being sold to Florida-based Airspan Networks in 2005. His group's work has been featured several times in IEEE Spectrum, MIT Technology Review, Scientific American, Yahoo! News, NPR, WHYY Radio, and recently also in Computerworld, Der Spiegel, London Times, International Business Times, Fortune, CBS Radio News, and over 300 other online venues and print media around the world.
Prof. Lindqvist is a recipient of the MobiCom'12 best paper award and the UbiComp'14 best paper nominee award.

Dr. Aleksandar Jovicic, Qualcomm

Tuesday, April 7, 2015 - 2:00pm - 3:00pm

CoRE Lecture Hall

Title: Visible Light Communication: Opportunities, Challenges and the Path to Market


With the rise of LED-based light fixtures and light bulbs as a means of providing general illumination, the door has been opened for a new form of wireless communication – visible light communication, or VLC. Having several properties that render it useful as a means of complementing and supplementing traditional radio frequency communication, VLC also uniquely enables novel technologies such as high accuracy indoor positioning of mobile devices in indoor environments. An accurate “indoor GPS” technology has been the holy grail of R&D programs across the wireless industry for over a decade because it can enable a host of compelling use cases centered around smartphone- and robot-navigation in several market sectors such as retail, enterprise and industrial.


Aleksandar Jovicic is a Systems Engineering Lead at Qualcomm Research in Bridgewater, New Jersey, where he has been since 2007. At Qualcomm, Aleksandar has been responsible for providing technical leadership for a number of R&D programs in indoor positioning, wireless communication and machine learning. He is currently heading the Lumicast program which has produced a novel high accuracy indoor positioning system based on visible light communication. Aleksandar has Ph.D. and M.S. degrees in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign, and a B.S. degree in ECE from University of Wisconsin-Madison. He is the recipient of the 2007 Robert T. Chien Award for Excellence in Research from the University of Illinois. His work has been published in IEEE Transactions of Information Theory and IEEE Transactions on Networking and has received over 1000 citations. He is the holder of 36 US patents and has 64 pending patent applications.

Dr. Ozan Tonguz, Carnegie Mellon University

Friday, April 3, 2015 - 2:30pm - 3:30pm

CAIT Building Auditorium


Traffic congestion in major cities is a chronic problem which is getting worse as the pace of urbanization around the world keeps increasing. It is clear that using the existing approaches for traffic control in urban areas is not effective. In this talk, we will highlight some of the new research ideas pursued at Carnegie Mellon University (CMU) for solving this problem. In particular, it will be shown that using vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications for controlling the traffic at intersections can increase traffic flows and reduce the commute time of urban workers significantly. This disruptive technology is known as Virtual Traffic Lights (VTL) and was invented in 2010. Subsequently, Virtual Traffic Lights, LLC, was founded in December 2010 as a CMU spinoff for solving some of the acute transportation problems in urban areas by using V2V and V2I communications paradigms based on the emerging Dedicated Short Range Communications (DSRC) technology at 5.9 GHz in addition to other wireless and cellular technologies. The proprietary VTL technology (U.S. Patent granted) is proven to increase the traffic flows in urban areas by up to 60% during rush hours which seems pretty significant and revolutionary. Such an improvement has serious implications in terms of reducing the commute time of urban workers, mitigating congestion, lessening the carbon footprint of cars, increasing productivity, and supporting a greener environment. In addition, the VTL technology will be an indispensable building block for the ongoing R&D efforts on autonomous driving pursued by Google and several car manufacturers.

(I dedicate this Invited Talk to the memory of my advisor, Professor David G. Daut)


Ozan K. Tonguz is a tenured Full Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU). Before joining CMU in August 2000, he was with the ECE Dept. of the State University of New York at Buffalo (SUNY/Buffalo). He joined SUNY/Buffalo in 1990 as an Assistant Professor, where he was granted early tenure and promoted to Associate Professor in 1995, and to Full Professor in 1998.

Prior to joining academia, he was with Bell Communications Research (Bellcore) between 1988-1990 doing research in optical networks and communication systems. His current research interests are in vehicular networks, sensor networks, computer networks, wireless networks and communications systems, self-organizing networks, smart grid, Internet of Things, optical communications and networks, bioinformatics, and security. He has published about 300 technical papers in IEEE journals and conference proceedings. He is well-known for his contributions to vehicular networks, wireless communications and networks, and optical communications and networks. He is the author (with G. Ferrari) of the 2006 Wiley book entitled “Ad Hoc Wireless Networks: A Communication-Theoretic Perspective”. He is the founder and President of Virtual Traffic Lights, LLC, a CMU start-up which was launched in December 2010 for providing solutions to several key transportation problems related to safety and traffic information systems, using vehicle-to-vehicle and vehicle-to-infrastructure communications paradigms. His industrial experience includes periods with Bell Communications Research, CTI Inc., Harris RF Communications, Aria Wireless Systems, Clearwire Technologies, Nokia Networks, Nokia Research Center, Neuro Kinetics, Asea Brown Boveri (ABB), General Motors (GM), Texas Instruments, and Intel. He currently serves or has served as a consultant or expert for several companies (such as Aria Wireless Systems, Harris RF Communications, Clearwire Technologies, Nokia Networks, Alcatel, Lucent Technologies), major law firms (Jones Day, WilmerHale, Williams and Connolly, Heller Ehrman, Baker Botts, Soroker-Agmon, Dinsmore&Shohl, Carlson and Caspers, etc.), and government agencies (such as NSF) in the USA, Europe, and Asia in the broad area of telecommunications and networking. He also served as the Co-Director (Thrust Leader) of the Center for Wireless and Broadband Networking Research at Carnegie Mellon University. More details about his research interests, research group, projects, and publications can be found at

Dr. Tonguz has served as an Associate Editor or Guest Editor for several IEEE Journals and Transactions (such as IEEE TRANSACTIONS ON COMMUNICATIONS, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, IEEE/OSA JOURNAL OF LIGHTWAVE TECHNOLOGY, IEEE COMMUNICATIONS MAGAZINE) and as a member of Technical Program Committees of several IEEE Conferences and Symposia (such as INFOCOM, SECON, GLOBECOM, ICC, VTC, WCNC, etc.).

Dr. Yahya Tousi, IBM TJ Watson Research Center

Wednesday, April 1, 2015 - 10:00am - 11:00am

CoRE Building Lecture Hall

Title: Collective Dynamical Circuits for Sub-mm-wave and Terahertz Electronics

Abstract: Within the last two decades, integrated electronics have been instrumental in realizing RF and mm-wave integrated systems and today we are in the vicinity of terahertz frequencies. From a technological point of view, mm-wave and sub-mm-wave electronics holds a plethora of opportunities for imaging, sensing, medical diagnosis, and wideband communication. For example, medical tests that rely on surface moisture or substance gradient such as dental cavities or skin damage can migrate from invasive X-ray exams to low cost terahertz chips. Short range and wideband communication, non-destructive industrial testing, and the detection of concealed objects are among other applications of T-rays.

Thus far, the device fmax has imposed a fundamental limit on the available power and operating frequency of solid-state sources. In this talk, I demonstrate how to exploit the theory of distributed dynamical systems to surmount the barrier of individual devices and achieve scalable terahertz radiation on a standard silicon process. First, I introduce delay-coupled oscillators, a novel architecture to efficiently generate, combine, and control the frequency of a sub-terahertz source. Next, I present an inherently scalable phased array platform based on the concept of collective dynamical circuits. Based on this approach, I demonstrate the first fully integrated terahertz source that can synthesize, radiate and steer terahertz waves all from a miniature silicon die, opening the door to a new wave of applications and technologies.

Bio: Yahya Tousi received his B.S. degree in 2004 and his M.S. degree in 2006 both in Electrical Engineering from Sharif University of Technology, Tehran, Iran. In 2012 he received the Ph.D. degree from the Department of Electrical and Computer Engineering at Cornell University, Ithaca, NY. Since 2014 he has been with the IBM T. J. Watson Research Center at Yorktown Heights, NY with a focus on mm-wave and terahertz integrated circuits and systems. Prior to that, from 2012 to 2014 he was with SiTune Corporation in San Jose, CA working integrated circuits for wideband satellite links. His research interests are in novel high-speed integrated circuits and systems for communication, biomedical, and signal processing applications.
Dr. Tousi is the recipient of the 2009 Cornell Jacob Fellowship, and the 2011 IEEE Microwave Theory and Techniques Society Graduate Fellowship. He is also the winner of the Graduate Research Competition at IMS 2011, and the recipient of the 2011-12 IEEE Solid-State Circuits Society Pre-Doctoral Achievement Award.

Dr. Alejandro Ribeiro, University of Pennsylvania

Wednesday, March 25, 2015 - 10:00am - 11:00am

CoRE Lecture Hall

Title: Axiomatic Construction of Hierarchical Clustering in Asymmetric Networks

Abstract: We consider networks where relationships between nodes are represented by directed dissimilarities. The goal is to study methods for the determination of hierarchical clusters, i.e., a family of nested partitions indexed by a connectivity parameter, induced by the given dissimilarity structures. Our construction of hierarchical clustering methods is based on defining admissible methods to be those methods that abide by the axioms of value -- nodes in a network with two nodes are clustered together at the maximum of the two dissimilarities between them -- and transformation -- when dissimilarities are reduced, the network may become more clustered but not less. Several admissible methods are constructed and two particular methods, termed reciprocal and nonreciprocal clustering, are shown to provide upper and lower bounds in the space of admissible methods. Alternative clustering methodologies and axioms are further considered. Allowing the outcome of hierarchical clustering to be asymmetric, so that it matches the asymmetry of the original data, leads to the inception of quasi-clustering methods. The existence of a unique quasi-clustering method is shown. Allowing clustering in a two-node network to proceed at the minimum of the two dissimilarities generates an alternative axiomatic construction. There is a unique clustering method in this case too. We will also discuss algorithms for the computation of hierarchical clusters using matrix powers on a min-max dioid algebra and studies the stability of the methods proposed. We prove that most of the methods introduced in the talk are such that similar networks yield similar hierarchical clustering results. Algorithms are exemplified through their application to networks describing internal migration within states of the United States (U.S.) and the interrelation between sectors of the U.S. economy.

Bio: Dr. Alejandro Ribeiro received the B.Sc. degree in electrical engineering from the Universidad de la Republica Oriental del Uruguay, Montevideo, in 1998 and the M.Sc. and Ph.D. degree in electrical engineering from the Department of Electrical and Computer Engineering, the University of Minnesota, Minneapolis in 2005 and 2007. From 1998 to 2003, he was a member of the technical staff at Bellsouth Montevideo. After his M.Sc. and Ph.D studies, in 2008 he joined the University of Pennsylvania (Penn), Philadelphia, where he is currently an Assistant Professor at the Department of Electrical and Systems Engineering. His research interests are in the applications of statistical signal processing to the study of networks and networked phenomena. His current research focuses on wireless networks, network optimization, learning in networks, networked control, robot teams, and structured representations of networked data structures. Dr. Ribeiro received the 2012 S. Reid Warren, Jr. Award presented by Penn's undergraduate student body for outstanding teaching, the NSF CAREER Award in 2010, and student paper awards at the 2013 American Control Conference (as adviser), as well as the 2005 and 2006

Dr. Sundeep Rangan, NYU Polytechnic School of Engineering

Wednesday, March 11, 2015 - 10:00am - 11:00am

CoRE Lecture Hall

Millimeter Wave Cellular Wireless Networks for 5G

With the severe spectrum shortage in conventional cellular bands, millimeter wave (mmWave) frequencies, roughly between 30 and 300 GHz, have attracted growing attention for next-generation micro- and picocellular wireless networks. The mmWave bands offer orders of magnitude greater spectrum than current cellular allocations and enable very high-dimension antenna arrays for further gains via beamforming and spatial multiplexing. However, the propagation of mmWave signals in outdoor non line-of-sight (NLOS) links remains challenging. In this talk, I use recent real-world measurements at 28 and 73 GHz in New York City to provide a realistic assessment of mmWave picocellular networks in a dense urban deployment. It is found that, even under conservative propagation assumptions, mmWave systems can offer an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks with similar cell density. However, reaching the full potential of the mmWave bands will require significant changes in the way networks are designed today and I will discuss various research efforts and preliminary results toward realizing mmWave 5G. Joint work with Elza Erkip & Ted Rappaport.

Sundeep Rangan received the B.A.Sc. at the University of Waterloo, Canada and the M.Sc. and PhD at the University of California, Berkeley, all in Electrical Engineering. He has held postdoctoral appointments at the University of Michigan, Ann Arbor and Bell Laboratories, Murray Hill, NJ. In 2000, he co-founded (with four others) Flarion Technologies, a spin off of Bell Labs, that developed Flash OFDM, one of the first cellular OFDM data systems and precursor to modern 4G system such as LTE. In 2006, Flarion was acquired by Qualcomm Technologies where Dr. Rangan was a Director of Engineering involved in OFDM infrastructure products. He joined the ECE department at NYU-Poly in 2010 as an Associate Professor. He is also the Associate Director of NYU WIRELESS -- an industry-academic partnership program for shaping 5G cellular. His research interests are in wireless communications, signal processing, information theory and control theory.

Dr. Mingyan Liu, University of Michigan

Wednesday, March 4, 2015 - 10:00am - 11:00am

CoRE Lecture Hall



A problem facing many crowdsourcing systems is the unknown and uncontrolled nature of the quality of data inputs. In some cases this quality is unknown but has an objective measure. Consider for instance the problem of labeling massive datasets using Amazon Mechanic Turks (AMTs) where each labeler has an unknown annotation quality. In some other cases this quality is not only unknown but has a subjective measure. Consider for instance the problem of using online recommendation systems to make decisions about movie, restaurant, shopping, news articles, and so on. In both cases a user (the one handing out the labeling tasks and the one seeking to make a decision by others' recommendations, respectively) is interested in knowing how to select the best labelers to perform the task or whose opinion and recommendation should be valued in making its own choice. We formulate this problem as a sequential decision and learning problem, where the user through feedback learns over time to gravitate toward a select subset, a "best crowd" of labelers or recommenders who provide the most value to the user. This type of online learning in some sense falls under the family of multi-armed bandit (MAB) problems, but with a distinct feature not commonly seen: since the labelers' or recommenders' quality is unknown, their input (or reward in the MAB context) has no way to be verified; thus it can only be estimated against the crowd or the user itself and only known probabilistically. Different from most literature in this area, this formulation allows us to develop algorithms that work in an online fashion (thus causally), but that can also be used in an offline (non-causal) setting. We will show that they can outperform existing offline solutions (such as matrix factorization-based methods).


Mingyan Liu received her Ph.D in electrical engineering from the University of Maryland, College Park, in 2000. She has since been with the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor, where she is currently a Professor. Her research interests are in optimal resource allocation, incentive design, and performance modeling and analysis, all within the context of communication networks. She is the recipient of the 2002 NSF CAREER Award, the University of Michigan Elizabeth C. Crosby Research Award in 2003 and 2014, the 2010 EECS Department Outstanding Achievement Award and the 2015 College of Engineering Excellence in Education Award. She holds Best Paper Awards from the International Conference on Information Processing in Sensor Networks (IPSN) in 2012 and the IEEE/ACM International Conference on Data Science and Advanced Analytics (DSAA) in 2014. She serves/has served on the editorial board of IEEE/ACM Trans. Networking, IEEE Trans. Mobile Computing, and ACM Trans. Sensor Networks. She is a Fellow of the IEEE and a member of the ACM.

Dr. Min Wu, University of Maryland

Friday, February 27, 2015 - 10:00am - 11:00am

CoRE Lecture Hall

Title: Exploring Power Network Signatures for Information Forensics


Osama bin Laden's video propaganda prompted numerous information forensic questions: given a video under question, when and where was it shot? Was the sound track captured together at the same time/location as the visual, or superimposed later? Similar questions about the time, location, and integrity of multimedia and other sensor recordings are important to provide evidence and trust in crime solving, journalism, infrastructure monitoring, smart grid management, and other informational operations.

An emerging line of research toward addressing these questions exploits novel signatures induced by the power network. An example is the small random-like fluctuations of the electricity frequency known as the Electric Network Frequency (ENF), owing to the dynamic control process to match the electricity supplies with the demands in the grid. These environmental signatures reflect the attributes and conditions of the power grid and become naturally embedded into various types of sensing signals. They carry time and location information and may facilitate integrity verification of the primary sensing data.

This talk will provide an overview of recent information forensics research on ENF carried out by our Media and Security Team (MAST) at University of Maryland, and discuss some on-going and open research issues in and beyond security applications.

Speaker's Bio:

Min Wu is an ADVANCE Professor of Electrical and Computer Engineering and a Distinguished Scholar-Teacher at the University of Maryland, College Park. She received her Ph.D. degree in electrical engineering from Princeton University in 2001. At UMD, she leads the Media and Security Team (MAST), with main research interests on information security and forensics and multimedia signal processing. Her research and education have been recognized by a NSF CAREER award, a TR100 Young Innovator Award from the MIT Technology Review Magazine, an ONR Young Investigator Award, a Computer World "40 Under 40" IT Innovator Award, a University of Maryland Invention of the Year Award, an IEEE Mac Van Valkenburg Early Career Early Career Teaching Award, and several paper awards from IEEE SPS, ACM, and EURASIP. She was elected IEEE Fellow for contributions to multimedia security and forensics. Dr. Wu chaired the IEEE Technical Committee on Information Forensics and Security (2012-2013), and has served as Vice President - Finance of the IEEE Signal Processing Society (2010-2012) and Founding Chief Editor of IEEE SigPort initiative (2013-2014). Currently, she is serving as Editor-in-Chief (2015-2017) of the IEEE Signal Processing Magazine and an IEEE Distinguished Lecturer.

Dr. Min Wu's website:

Dr. Min Wu's presentation slides for her talk.

Dr. Moeness Amin, Villanova University

Wednesday, February 25, 2015 - 10:00am - 11:00am

CoRE Lecture Hall

Title/Abstract: A Sparsity-Perspective to Time-Frequency Signal Representations

The talks considers nonstationary signal analysis in view of the signal sparsity properties. We examine these signals, which arise in numerous applications, within the framework of compressive sensing (CS) and sparse reconstructions. We present two general approaches to incorporate sparsity into time-frequency signal presentation (TFSR). In the first approach, quadratic TF distributions (QTFDs) are derived based on optimal multi-task kernel design. In this case, sparseness in the TF domain presents itself as a new design task, adding to those related auto-term preservation and cross-term suppression. In contrast to QTFDs, we also provide a second approach for signal TF signature estimation where sparse reconstruction is used in lieu of direct Fourier transform that maps the signal from the time or one joint-variable domain to another. It is shown that multiple measurement vector methods and block sparsity techniques play a clear and fundamental role in improving signal local power representations. Examples of both approaches are provided. Analysis is supported by simulations with synthesized data, experiments with real Doppler and microDoppler data measurements of radar returns associated with human motions, and by electromagnetic modeling.


Dr. Amin is the Director of the Center for Advanced Communications, Villanova University, Pennsylvania, USA. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE); Fellow of the European Association for Signal Processing (EURASIP); Fellow of the International Society of Optical Engineering; and a Fellow of the Institute of Engineering and Technology (IET). Dr. Amin is the Recipient of the 2014 IEEE Signal Processing Society Technical Achievement Award; Recipient of the 2009 Individual Technical Achievement Award from EURASIP; Recipient of the 2010 NATO Scientific Achievement Award; Recipient of the 2010 Chief of Naval Research Challenge Award; Recipient of 1997 Villanova University Outstanding Faculty Research Award; and the Recipient of the 1997 IEEE Philadelphia Section Award. He is a Recipient of the IEEE Third Millennium Medal, and was a Distinguished Lecturer of the IEEE Signal Processing Society, 2003-2004. Dr. Amin is currently the Chair of the Electrical Cluster of the Franklin Institute Committee on Science and the Arts. He has over 700 journal and conference publications in the areas of Wireless Communications, Time-Frequency Analysis, Sensor Array Processing, Waveform Design and Diversity, Interference Cancellation in Broadband Communication Platforms, Satellite Navigations, Target Localization and Tracking, Direction Finding, Channel Diversity and Equalization, Ultrasound Imaging and Radar Signal Processing. He co-authored 18 book chapters. He is the Editor of the two books "Through the Wall Radar Imaging" and "Compressive Sensing for Urban Radar," published by CRC Press in 2011 and 2014, respectively.

Dr. Shlomo Shamai, Technion – Israel Institute of Technology

Wednesday, February 11, 2015 - 10:00am - 11:00am

CoRE Lecture Hall

Abstract: The timely concept of cognitive radio networks will be reviewed, emphasizing the information theoretic viewpoint. Following a short introduction reviewing standard approaches, we focus on the information-theoretic paradigm, We examine simple models, under idealized assumptions, highlighting the impact of cognition and cooperation. Theoretical results and their implications are demonstrated by examining specific problems, namely: Generalized encoding schemes, accounting for the coding structure of the primary user; Simple outer bounds on rate regions; Capacity of the cognitive setting in the degraded message set case, including states and action; Results for a class of Z-channels and Cognitive Wyner based cellular models. A short summary and an outlook conclude the overview. The technical contributions of collaborators in joint research on cognitive radio is gratefully acknowledged

Bio: Dr. Shlomo Shamai (Shitz) received the B.Sc., M.Sc., and Ph.D. degrees in electrical engineering from the Technion---Israel Institute of Technology, in 1975, 1981 and 1986 respectively. During 1975-1985 he was with the Communications Research Labs, in the capacity of a Senior Research Engineer. Since 1986 he is with the Department of Electrical Engineering, Technion---Israel Institute of Technology, where he is now a Technion Distinguished Professor, and holds the William Fondiller Chair of Telecommunications. His research interests encompasses a wide spectrum of topics in information theory and statistical communications.

Dr. Shamai (Shitz) is an IEEE Fellow, a member of the Israeli Academy of Sciences and Humanities and a foreign member of the US National Academy of Engineering. He is the recipient of the 2011 Claude E. Shannon Award and the 2014 Rothschild Prize in Mathematics/Computer Sciences and Engineering. He has been awarded the 1999 van der Pol Gold Medal of the Union Radio Scientifique Internationale (URSI), and is a co-recipient of the 2000 IEEE Donald G. Fink Prize Paper Award, the 2003, and the 2004 joint IT/COM societies paper award, the 2007 IEEE Information Theory Society Paper Award, the 2009 European Commission FP7, Network of Excellence in Wireless COMmunications (NEWCOM++) Best Paper Award, and 2014 EURASIP Best Paper Award (for the EURASIP Journal on Wireless Communications and Networking). He is also the recipient of the 2010 Thomson Reuters Award for International Excellence in Scientific Research and is listed in the 2014 Thomson Reuters "The World's Most Influential Scientific Minds".‘ He is also the recipient of 1985 Alon Grant for distinguished young scientists and the 2000 Technion Henry Taub Prize for Excellence in Research. He has served on the Executive Editorial Board of the IEEE Transactions on Information Theory and has also served as a Shannon Theory Associate Editor for this journal. He has served twice on the Board of Governors of the Information Theory Society.

Dr. Sean Smith, Dartmouth College

Wednesday, December 3, 2014 - 10:00am - 11:00am

CoRE Lecture Hall

Dr. Joseph Bonneau, Princeton University

Wednesday, November 19, 2014 - 10:00am - 11:00am

CoRE Building Lecture Hall

Title:   Storing 56-bit keys in human memory


The talk will challenge conventional wisdom that users cannot remember cryptographically-strong secrets. We tested the hypothesis that users can learn randomly-assigned 56-bit codes (encoded as either 6 words or 12 characters) through spaced repetition. We asked remote research participants to perform a distractor task that required logging into a website 90 times over up to two weeks with a password of their choosing. After they entered their password correctly we displayed a short code (4 letters or 2 words, 18.8 bits) that we required them to type. For subsequent logins we added an increasing delay prior to displaying the code, which they could avoid by typing the code from memory. As participants learned, we added two more codes to comprise a 56.4-bit secret. Overall, 94% of participants eventually typed their entire secret from memory, learning it after a median of 36 logins. The learning component of our system added a median delay of just 6.9 s per login and a total of less than 12 minutes over an average of ten days. 87% were able to recall their codes exactly when asked at least three days later, with only 21% reporting having written their secret down. As one participant wrote with surprise, “the words are branded into my brain.” This talk will overview the potential of training users to memorize strong random passwords for high-security applications.


Joseph Bonneau is a Postdoctoral Research Fellow at the Center for Information Technology Policy, Princeton. His research interests include passwords and web authentication, Bitcoin and cryptocurrencies, HTTPS, and secure messaging software. He received a PhD from the University of Cambridge under the supervision of Ross Anderson and an MS from Stanford under the supervision of Dan Boneh. He has worked at Google, Yahoo, and Cryptography Research Inc.

Dr. Gang Qu, University of Maryland

Wednesday, November 12, 2014 - 10:00am - 11:00am

CoRE Building Lecture Hall


It is well-known that hardware implementation can outperform the software implementation of most applications, including security primitives such as encryption, by up to several order of magnitudes. However, hardware implementation may also make these mathematically sound security primitives vulnerable. In this talk, we will discuss the role of hardware in cybersecurity. First, we will use the finite state machine (FSM) model to demonstrate that systems built with today's design flow and tools are vulnerable against a simple random walk attack. We further show that a malicious designer can embed Hardware Trojan Horse (HTH) into the system to gain unauthorized control of the system. We then describe a practical circuit level technique to guarantee the trustworthiness of the circuit implementation of a given FSM. Second, we describe our recent work on physical unclonable function (PUF), a unique feature embedded in the chip during fabrication process. PUF has many promising applications in security and trust such as device authentication and secret key generation and storage. We will focus on the usability problems of PUF: how to push the amount of PUF information we can extract to the theoretical upper bound; how to ensure that the PUF information is random (and thus secure against attacks); how to improve the hardware efficiency when implementing a PUF. Finally, we will show very briefly a couple of our projects on hardware-software co-design in building security and trust to demonstrate the great promise that hardware can bring to cybersecurity.


Gang Qu received his Ph.D. degree in computer science from the University of California, Los Angeles, in 2000. He is currently a professor in the Department of Electrical and Computer Engineering and Institute for Systems Research, University of Maryland at College Park. He is also a member of the Maryland Cybersecurity Center and the Maryland Energy Research Center. Dr. Qu is the director of Maryland Embedded Systems and Hardware Security (MeshSec) Lab and the Wireless Sensors Laboratory. His primary research interests are in the area of embedded systems and VLSI CAD with focus on low power system design and hardware related security and trust. He studies optimization and combinatorial problems and applies his theoretical discovery to applications in VLSI CAD, wireless sensor network, bioinformatics, and cybersecurity.

> ________________________________

Dr. Hagit Messer, Tel Aviv University

Friday, November 7, 2014 - 10:00am - 11:00am

CAIT Auditorium


While much effort is being put into building special-purpose wireless sensor networks (WSN) for environmental monitoring, the use of existing measurements from commercial wireless communication systems is suggested as an opportunistic sensors network for environmental monitoring. In particular, we propose using measurements of the received signal level (RSL) in the backhaul communication microwave network (CMN) of cellular systems. Recent results have demonstrated the use of the suggested technique for estimating and mapping of rain, as well as of monitoring other-than-rain phenomena, such as: snow and sleet, moisture, for and dew. However, existing measurements of the RSL in a commercial communication microwave network go through non-linear preprocessing, matched to the needs of the cellular providers. Typically, the signals are roughly quantized, and in most protocols the min/max RSL values over each period of 15 minutes is stored, instead of instantaneous samples of the RSL. I will discuss the challenges in adopting statistical signal processing tools to deal with such limitations for optimally detect precipitation, estimate the accumulated rainfall, and constructing instantaneous rain maps by estimating the parameters of the rain fields.

Dr. Messer's presentation slides: "Commercial Wireless Communication Microwave Links As An Opportunistic Sensor Network For Environmental Monitoring"


Dr. HAGIT MESSER, Fellow of the IEEE, has joined the faculty of Engineering at Tel Aviv University (TAU) in 1986, after post-doctorate at Yale University, where she is a professor of Electrical Engineering. On 2000 - 2003 she has been on leave from TAU, serving as the Chief Scientist at the Ministry of Science, ISRAEL. After returning to TAU she was the head of the Porter school of environmental studies (2004-6), and the Vice President for Research and Development 2006-8. On October 2008 she has started a 5 years term as the President of the Open University in Israel. She is now back to Tel Aviv University, also serving as the Vice-Chair of the council for Higher Education, ISRAEL

Dr. Shri Narayanan, University of Southern California

Wednesday, November 5, 2014 - 10:00am - 11:00am

CoRE Building Lecture Hall

Shrikanth (Shri) Narayanan
University of Southern California, Los Angeles, CA
Signal Analysis and Interpretation Laboratory

Audio-visual data have been a key enabler of human behavioral research and its applications. The confluence of sensing, communication and computing technologies is allowing capture and access to data, in diverse forms and modalities, in ways that were unimaginable even a few years ago. Importantly, these data afford the analysis and interpretation of multimodal cues of verbal and non-verbal human behavior. These data sources carry crucial information about not only a person’s intent and identity but also underlying attitudes and emotions. Automatically capturing these cues, although vastly challenging, offers the promise of not just efficient data processing but in tools for discovery that enable hitherto unimagined insights.

Recent computational approaches that have leveraged judicious use of both data and knowledge have yielded significant advances in this regards, for example in deriving rich, context-aware information from multimodal sources including human speech, language, and videos of behavior. These are even complemented and integrated with data about human brain and body physiology. This talk will focus on some of the advances and challenges in gathering such data and creating algorithms for machine processing of such cues. It will highlight some of our ongoing efforts in Behavioral Signal Processing (BSP)—technology and algorithms for quantitatively and objectively understanding typical, atypical and distressed human behavior—with a specific focus on communicative, affective and social behavior. The talk will illustrate Behavioral Informatics applications of these techniques that contribute to quantifying higher-level, often subjectively described, human behavior in a domain-sensitive fashion. Examples will be drawn from health and well being realms such as Autism, Couple therapy, Depression and Addiction counseling.

Biography of the Speaker:
Shrikanth (Shri) Narayanan is Andrew J. Viterbi Professor of Engineering at the University of Southern California, where he is Professor of Electrical Engineering, Computer Science, Linguistics and Psychology and Director of the Ming Hsieh Institute. Prior to USC he was with AT&T Bell Labs and AT&T Research. His research focuses on human-centered information processing and communication technologies. He is a Fellow of the Acoustical Society of America, IEEE, and the American Association for the Advancement of Science (AAAS). Shri Narayanan is an Editor for the Computer, Speech and Language Journal and an Associate Editor for the IEEE Transactions on Affective Computing, the Journal of Acoustical Society of America and the APISPA Transactions on Signal and Information Processing having previously served an Associate Editor for the IEEE Transactions of Speech and Audio Processing (2000-2004), the IEEE Signal Processing Magazine (2005-2008) and the IEEE Transactions on Multimedia (2008-2012). He is a recipient of several honors including the 2005 and 2009 Best Transactions Paper awards from the IEEE Signal Processing Society and serving as its Distinguished Lecturer for 2010-11. With his students, he has received a number of best paper awards including winning the Interspeech Challenges in 2009 (Emotion classification), 2011 (Speaker state classification), 2012 (Speaker trait classification), 2013 (Paralinguistics/Social Signals) and in 2014 (Paralinguistics/Cognitive Load). He has published over 600 papers and has been granted 16 U.S. patents.

Dr. James Hwang, Lehigh University

Wednesday, October 29, 2014 - 10:00am - 11:00am

CoRE Building Lecture Hall

Abstract – Traditionally, cell detection is accomplished through chemical or optical means for which sophisticated instruments such as DNA sequencers or flow cytometers are commercially available. DNA sequencers can be very specific, but are slow and destructive. Optical cytometers can be fast and sensitive to single cells and their vitality, but often require labeling which may alter their physiological state. In comparison, electrical cell detection can be label-free and nondestructive with high throughput. To this end, cytometers capable of measuring the electrical properties of single cells are also commercially available as Coulter counters. However, they can suffer from the dilemma of cell clogging or solution parasitics. Cell clogging occurs if a narrow channel is used to increase the cell-to-sample volume ratio, whereas solution parasitics are aggravated if a wide channel is used to prevent cell clogging. Additionally, Coulter counters typically use discrete frequencies on the order of MHz or lower, which made them unduly sensitive to the size and shape variations of individual cells, as well as the polarization layers formed in the solution between the cells and electrodes. For these reasons, Coulter counters are usually optimized for a special purpose such as for counting human blood cells. Recently, to resolve the dilemma encountered by Coulter counters and to evolve a general- purpose electrical detection technique, we used broadband microwave measurement to overcome electrode polarization, AC dielectrophoresis to precisely place cells between narrowly spaced electrodes for maximum cell-to-sample volume ratio, and relatively wide microfluidic channels to prevent cell clogging. This unique combination of approaches resulted in reproducible sensing of single Jurkat and HEK cells, both live and dead, of different cultures at different times.

Bio: Dr. James Hwang is Professor of Electrical Engineering and Director of Compound- Semiconductor Technology Laboratory at Lehigh University. He graduated with a B.S. degree in Physics from National Taiwan University in 1970, and completed M.S. (1973) and Ph.D. (1976) studies in Materials Science at Cornell University. After twelve years of industrial experience at IBM, AT&T, GE, and GAIN, he joined Lehigh in 1988. He cofounded GAIN and QED; the latter became a public company (IQE). He has been a Nanyang Professor at Nanyang Technological University in Singapore, as well as an advisory professor at Shanghai Jiao Tong University, East China Normal University, and University of Science and Technology in China. Most recently, he was a Program Officer for GHz-THz Electronics at the Air Force Office of Scientific Research. He is a fellow of the Institute of Electrical and Electronic Engineers. He has published ~300 refereed technical papers and has been granted five U. S. patents.

Prof. Anand Sarwate, Rutgers Department of Electrical & Computer Engineering

Wednesday, October 22, 2014 - 10:00am - 11:00am

CoRE Building Lecture Hall

Learning From Distributed Private Data: Algorithms and Applications

Distributed learning from biomedical data is often hindered by ethical, legal, and technological concerns about data sharing. Data holders wish to maintain control over the uses of their data, and patients or study subjects may be hesitant to allow free and open use of their private medical data. Differential privacy is a framework which allows the quantification of privacy risk. In privacy-preserving distributed learning, the data stays at each site: they locally compute a privacy-preserving summary of their information. The summaries are sent to a private aggregator that performs the final analysis. Differentially private algorithms guarantee privacy by deliberately introducing some noise into the computation - the uncertainty from the noise masks individual data points. This leads to a tradeoff between privacy and accuracy. In this talk I will discuss algorithms for privacy-preserving learning as well as a recent proof-of-concept for this approach applied to neuroimaging data for mental health research.

Anand D. Sarwate joined as an Assistant Professor in the Department of Electrical and Computer Engineering at Rutgers, the State University of New Jersey in 2014. He received B.S. degrees in Electrical Engineering and Mathematics from MIT in 2002, an M.S. in Electrical Engineering from UC Berkeley in 2005 and a PhD in Electrical Engineering from UC Berkeley in 2008. From 2008-2011 he was a postdoctoral researcher at the Information Theory and Applications Center at UC San Diego and from 2011-2013 he was a Research Assistant Professor at the Toyota Technological Institute at Chicago, a philanthropically endowed academic computer science institute located on the University of Chicago campus.

Dr. Serge Egelman, UCB and ICSI

Wednesday, October 1, 2014 - 10:00am - 11:00am

CoRE Building Lecture Hall


Mobile platforms employ permission-granting mechanisms so that users can exert control over how third-party applications access their personal data. Some platforms take a paternalistic approach by relying on a review process before an application can be approved for public consumption. At the opposite end of the spectrum, other platforms aim for transparency by presenting users with a list of requested permissions every time an application is installed. The former approach is opaque and does not allow users to understand how their data will be used, whereas the latter approach results in habituation when users are bombarded with requests they either do not understand or do not find concerning. In this talk, I discuss how balancing transparency with concerns over habituation empowers users to make better decisions about their privacy and security. Specifically, I describe previous and ongoing human subjects research to replace unnecessary permission requests with audit mechanisms, how to improve necessary permission requests, as well as how to tell the difference.


Dr. Serge Egelman is a research scientist with joint appointments in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley, and the International Computer Science Institute (ICSI). His research focuses on usable privacy and security, with the specific aim of better understanding how people make decisions surrounding their privacy and security, and then creating improved interfaces that better align stated preferences with outcomes. This has included human subjects research on social networking privacy, access controls, authentication mechanisms, web browser security warnings, and privacy-enhancing technologies. He received his PhD from Carnegie Mellon University and prior to that was an undergraduate at the University of Virginia. He has also performed research at NIST, Brown University, Microsoft Research, and Xerox PARC.

Prof. Xiang-Yang Li, Illinois Institute of Technology

Wednesday, September 17, 2014 - 10:00am - 11:00am

CoRE Building Lecture Hall


In many applications, we have to identify an object and then locate the object to within centimeter- or millimeter-level accuracy. Legacy systems that can provide such accuracy are either expensive or suffer from performance degradation resulting from various impacts, e.g., occlusion for computer vision based approaches. In this talk, we present an RFID-based system, Tagoram, for object localization and tracking using COTS RFID tags and readers. Tracking mobile RFID tags in real time has been a daunting task, especially challenging for achieving millimeter-level accuracy. Our system achieves these goals by leveraging the phase value of the backscattered signal, provided by the COTS RFID readers, to estimate the location of the object. In Tagoram, we exploit the tag’s mobility to build a virtual antenna array by using readings from a few physical antennas over a time window. Our system is robust to device diversity and multipath impact.

We have implemented the Tagoram system using COTS RFID tags and readers. The system has been tested extensively in the lab environment and used for more than a year in real airline applications. For lab environment, we can track the mobile tags in real time with accuracy to a median of 5mm along the moving direction. In our year-long large-scale trial studies in real luggage sortation systems of two airports, our results show that Tagoram can achieve accuracy to a median of 63.5mm in these real deployments. We also will show other related applications using our techniques.

Bio of Prof. XiangYang Li:

Dr. Xiang-Yang Li is a professor at Computer Science Department of IIT, and EMC Visiting Chair Professor at Tsinghua University. He was an Associate Professor (from 2006 to 2012) and Assistant Professor (from 2000 to 2006) of Computer Science at the Illinois Institute of Technology.

He is recipient of China NSF Outstanding Overseas Young Researcher (B). Dr. Li received MS (2000) and PhD (2001) degree at Department of Computer Science from University of Illinois at Urbana-Champaign. He received a Bachelor degree at Department of Computer Science and a Bachelor degree at Department of Business Management from Tsinghua University, P.R. China, both in 1995. He published a monograph "Wireless Ad Hoc and Sensor Networks: Theory and Applications". He also co-edited the book "Encyclopedia of Algorithms". The research of Dr. Li has been supported by USA NSF, HongKong RGC, and China NSF.

His research interests include the cyber physical systems, wireless networks, mobile computing, privacy and security, social networking, and algorithms. He has published more than 120 papers in top-tier journals, and 190 papers in well-known international conferences. His Google-scholar citation is more than 10,000, and H-index is 50. Dr. Li has served or is serving as an editor of several journals, including IEEE Transaction on Parallel and Distributed Systems, IEEE Transaction on Mobile Computing. He served at various capacities (conference chair, TPC chair, or local arrangement chair) in a number of conferences, including ACM MobiHoc 2014. His research has been supported by NSF, NSFC, and RGC HongKong. He has graduated eleven PhD students since 2004. For more information about Prof. XiangYang Li, please check his webpage and

Prof. Janne Lindqvist, Rutgers Department of Electrical & Computer Engineering

Wednesday, September 10, 2014 - 10:00am - 11:00pm

CoRE Building Lecture Hall

Title: Human-Computer Interaction, Security and Privacy

In this talk, we will discuss two of our recent works on using methods from human-computer interaction for security and privacy. First, we discuss Elastic Pathing, an algorithm that can deduce your driving locations just based on a starting location and the speed of your driving. Second, we will discuss a transformative approach to user authentication: user-generated free-form gestures and their security and memorability.

Janne Lindqvist is an assistant professor of electrical and computer engineering and a member of WINLAB at Rutgers University.
From 2011-2013, Janne was an assistant research professor at WINLAB/ECE at Rutgers. Prior to Rutgers, Janne was a post-doc with the Human-Computer Interaction Institute at Carnegie Mellon University's School of Computer Science. Janne received his M.Sc. degree in 2005, and D.Sc. degree in 2009, both in Computer Science and Engineering from Helsinki University of Technology, Finland. He works at the intersection of human-computer interaction, mobile computing and security engineering. Before joining academia, Janne co-founded a wireless networks company, Radionet, which was represented in 24 countries before being sold to Florida-based Airspan Networks in 2005. His work has been featured several times in IEEE Spectrum, MIT Technology Review, Scientific American, Yahoo! News and recently also in Computerworld, Der Spiegel, London Times, International Business Times, Fortune, CBS Radio News, WHYY Radio, and over 300 other online venues and print media around the world.

Vish Ishaya - Nebula, Chief Technical Officer

Friday, May 2, 2014 - 3:00pm - 4:00pm

CoRE Lecture Hall

Title: OpenStack and the Unique Challenges of Running a Cloud in a Next Generation Data Center


Discussion on OpenStack and the unique challenges of running a cloud in a next generation data center. Cloud development methodologies mean different approaches to problems. These approaches bring with them a new set of concerns. Why businesses and universities are deploying and adopting private clouds. OpenStack is a cloud toolkit, so the early-adopters are building and leveraging private clouds. These tend to be leading universities, DOD, service providers and large enterprises. In addition, high growth internet businesses that need to scalability and agility. These companies started solving business problems using virtualization and public clouds but are now looking for cloud flexibility and functionality in their own data centers,
under their control and behind the firewall.


Vish Ishaya, Nebula CTO - Vish Ishaya was previously Nebula's Director of Open Source and prior to that was a Principal Engineer with Rackspace Cloud Builders. He was also a Senior Systems Engineer with Anso Labs and NASA Nebula Technical Lead during the creation of Nova, one of the founding OpenStack projects. He is a highly prolific developer who was one of the founding engineers and a top contributor to OpenStack. During the November 2010 OpenStack conference, he won an OpenStack award for his development and community efforts, and was also elected to the first OpenStack Technical Committee, where he has served consistently since.

Vish was elected to four consecutive terms as the OpenStack Compute Project Technical Lead and is currently on the board of OpenStack Foundation. In addition to his programming and systems skills, Vish has spent over a decade teaching, most recently classes in object oriented analysis and design.

Dr. Seongshik Oh, Rutgers, Department of Physics & Astronomy

Wednesday, April 30, 2014 - 10:00am - 11:00am

CoRE Building Lecture Hall

Title: Atomic-layer-by-layer molecular beam epitaxy for topological insulators and artificial functional oxides


Molecular beam epitaxy (MBE), which was invented in late 1960s in Bell lab and advanced over the past decades, allows growth of various thin film structures with atomic precision. In particular, it led to a number of major breakthroughs in semiconductor systems such as fractional quantum Hall effect, high electron mobility transistor (HEMT) and quantum cascade lasers. In our lab, we are applying this MBE technology beyond the conventional semiconductors to investigate novel materials such as topological insulators and artificial functional oxides that are either difficult or impossible to grow using other methods. Topological insulators (TI) are a new class of materials that were discovered several years ago and are being heavily investigated, yet mostly in the physics community. One of the most intriguing properties of TIs is that they are supposed to have highly mobile metallic surface states while inside of the material is insulating. However, real TIs suffer from serious material problems that make the utilization of these surface states nearly impossible. In this talk, I will overview the MBE technology and how it can be used to study topological insulators and functional oxides. Then, I will discuss how we utilize this technique to overcome the material problems of TIs and implement various novel TI platforms.


Seongshik Oh received his Ph.D. in Physics from University of Illinois, Urbana-Champaign in 2003 and was a postdoctoral researcher at National Institute of Standards and Technology, Boulder between 2004 and 2007. In 2007 he joined the faculty of Department of Physics and Astronomy at Rutgers University, where he is currently an Associate Professor. His research interests are in synthesis and electronic properties of two dimensional quantum materials and devices. He received the NSF CAREER award and published over 50 journal articles and book chapters. More details can be found in his group website:

Dr. Gil Zussman, Columbia University

Wednesday, April 9, 2014 - 10:00am - 11:00am

CoRE Building Lecture Hall

Energy Harvesting Active Networked Tags (EnHANTs) — Measurements, Algorithms, and Prototyping


We discuss a new type of wireless devices in the domain between RFIDs and sensor networks – Energy Harvesting Active Networked Tags (EnHANTs - Future EnHANTs will be small, flexible, and self-powered devices that can be attached to objects that are traditionally not networked (e.g., books, toys, clothing), thereby providing the infrastructure for various Internet-of-Things tracking applications. We describe the paradigm shifts associated with the underlying enabling technologies. Then, we present the results of an indoor light energy measurement campaign and of a kinetic energy study that have been conducted in order to characterize the energy availability for EnHANTs. We discuss low complexity energy-harvesting-adaptive algorithms which aim to allocate resources uniformly in respect to time and to determine energy and data rate allocations for a node and for a link. Finally, we present the design considerations for the EnHANT prototypes which harvest indoor light energy using custom organic solar cells, communicate and form multihop networks using ultralow-power Ultra-Wideband Impulse Radio (UWB-IR) transceivers, and adapt their communications and networking patterns to the energy harvesting and battery states. We also describe a small scale EnHANTs testbed that uniquely allows evaluating different algorithms with trace-based light energy inputs and discuss experimental results.

Based on joint works with A. Bernstein, M. Gorlatova, R. Margolies, A. Wallwater, and the groups of P. Kinget, J. Kymissis, D. Rubenstein, and L. Carloni (Columbia).


Gil Zussman received the Ph.D. degree in electrical engineering from the Technion in 2004 and was a postdoctoral associate at MIT between 2004 and 2007. In 2008 he joined the faculty of the Department of Electrical Engineering at
Columbia University where he is currently an Associate Professor. His research interests are in the areas of wireless, mobile, and resilient networks. He is a co-recipient of 5 paper awards, including the ACM SIGMETRICS'06 Best Paper Award and the 2011 IEEE Communications Society Award for Outstanding Paper on New Communication Topics. He received the Fulbright Fellowship, two Marie Curie Fellowships, the DTRA Young Investigator Award, and the NSF CAREER Award. He was also the PI of a team that won first place in the 2009 Vodafone Americas Foundation Wireless Innovation Project competition.

J. Rockey Luo, Associate Professor, ECE, Colorado State University

Wednesday, April 2, 2014 - 10:00am - 11:00am

CoRE Building Lecture Hall

Coding Theory and Access Control for Distributed Wireless Networking


Classical network architecture assumes that communication optimization should be done at the physical layer. Data link layer only determines whether and when users should transmit packets. In a multiuser system, under the assumption that data link layer should efficiently schedule multiuser communication activities, classical channel coding has been focusing on coordinated communication scenarios where users jointly optimize their channel codes and communication parameters. As wireless networks are getting increasing complex and dynamic, scheduling the communication of a large number of users in a long time duration can be very difficult. Consequently, a significant proportion of messages in wireless networks are transmitted using distributed communication protocols. In these scenarios, users are not fully coordinated to optimize communication at the physical layer, and therefore data link layer should often get involved in communication adaptation. Unfortunately, not only that distributed communication is not well supported by classical channel coding theory at the physical layer, classical link layer model is also not able to support advanced communication adaptation due to the simple binary transmit/idle options of the users.

In this talk, we present a Shannon-style channel coding theory developed for distributed communication systems where users do not jointly design channel codes. We show that fundamental performance limitation of a distributed multiple access system can be characterized using an achievable region defined in a sense quite different from the classical ones. The new channel coding theory, which can indeed be viewed as an extension of the classical Shannon theory, enabled an enhanced physical-link layer interface where link layer users can now be equipped with more than two options to transmit their packets. At the data link layer, we formulate the distributed channel access problem as a non- coorperative game where each user optimizes an individual utility function. By assuming a general form of the utility functions, conditions under which the distributed channel access game possesses a unique Nash equilibrium is obtained. Simulation results show that when link layer users are provided with multiple transmission options, their behavior in distributed communication adaptation, as an outcome of the non-cooperative channel access game, tend to agree with the well known information theoretic understandings.


J. Rockey Luo received the Ph.D. degree in Electrical and Computer Engineering from University of Connecticut in 2002. From 2002 to 2006, he was a Research Associate with the Institute for Systems Research (ISR), University of Maryland, College Park. Since 2006, he has been with the Electrical and Computer Engineering Department of Colorado State University where he is currently an Associate Professor. His areas of research interests are communication networks, information theory, and signal processing.

Dr. George Celler, Rutgers University, IAMDN

Wednesday, March 26, 2014 - 10:00am - 11:00am

CoRE Building Lecture Hall

Semiconductor Technology Roadmap, its purpose and some examples

Abstract:   Global semiconductor business exceeds $350B in annual revenues, and semiconductor processing equipment adds additional $50B/year. Even though the technology companies fiercely compete with each other, there is also a lot of interdependence, as nobody can do it alone. Each semiconductor manufacturer depends on a vast infrastructure of hardware and software suppliers. In this highly dynamic environment, rapid progress as marked by Moore's law that predicts (and effectively requires) doubling of integrated circuit performance every two years would not be possible without a large degree of coordination between all the technology participants. For this purpose a roadmapping organization was formed in 1991, and it has evolved into a large team effort that develops and maintains the International Technology Roadmap for Semiconductors (ITRS).

I will describe the motivation for the ITRS and related roadmapping activities and how the roadmap is developed. Then I will provide some examples of what the ITRS is predicting for the future electronic devices

Biography:   Dr. George K Celler received his Ph.D. in physics from Purdue University. He is a Research Professor at the Materials Science and Engineering Dept. and the Institute for Advanced Materials, Devices, and Nanotechnology (IAMDN) at Rutgers University. Previously he was Chief Scientist at Soitec USA, where he was responsible for technical interactions and collaborations with the US industry and academia in the field of substrate engineering. Before joining Soitec in 2001, he spent 25 years at Bell Laboratories, where he was a Distinguished Member of Technical Staff and Technical Manager. In addition to his long-term interest in silicon-on-insulator structures and their applications, he also investigated laser annealing and rapid thermal processing of semiconductors, diffusion phenomena in Si and silicon dioxide, and electro-optical properties of GaAs. He led a large DARPA supported x-ray lithography program at Bell Labs and was a Deputy Manager of X-Ray Lithography Consortium. He published over 200 articles, edited nine books, and was issued 22 US patents. He is a fellow of the American Physical Society and of The Electrochemical Society, and a member of IEEE, OSA, and MRS. From 2002 to 2012 he led a SOI subcommittee of the International Technology Roadmap for Semiconductors (ITRS) and a SOI Standards subcommittee of SEMI. He is a member of the Board of MIT Microphotonics Consortium and a member of External Advisory Board of the NSF-funded MRSEC at Georgia Tech. on epitaxial graphene. He is an Associate Editor of the ECS Journal of Solid State Science and Technology, and the ECS Solid State Letters.

Dr. Jose Moura, Carnegie Mellon University

Wednesday, February 26, 2014 - 10:00am - 11:00am

CoRE Building Lecture Hall

Data & Models: Correlation and Causation

A few years ago Chris Anderson, Editor in Chief of Wired Magazine, proposed the “End of Theory;” no need for causation (modeling), from data (big) with Google smarts (or the likes) get all the correlations needed . We live in a time of great challenge and opportunity. Data is big, but, more importantly, comes in all sorts of ways and from many different sources – social, physical, biological, molecular, to name a few. However, if we do capture the relations among data through (arbitrary) graphs (and this in itself is a big if), we can recapture the “big data” challenge in the very familiar setting of everyone’s beloved DSP. This talk will overview our progress so far extending to signals and data defined on graphs traditional signal processing concepts including shifting, frequency, filtering, convolution, spectral representation, filters frequency response, linear transforms like the discrete Fourier transform. With a proper setting, models still make sense and help interpret the data. We illustrate with examples drawn from social networks and the World Wide Web.

Work with Dr. Aliaksei Sandryhaila and graduate student Jonathan Mei.

José M. F. Moura is a visiting Professor at CUSP, NYU (2013-14). He is the Philip and Marsha Dowd University Professor at Carnegie Mellon University, with interests in statistical signal processing (SP) and distributed SP on graphs. He was an IEEE Board Director (2013-2014), he was President of the IEEE Signal Processing Society (SPS), and was Editor in Chief for the Transactions on SP. Moura received several awards including the IEEE Signal Processing Society Technical Achievement Award and the IEEE Signal Processing Society Society Award for outstanding technical contributions and leadership in SP. He is a Fellow of the IEEE, a Fellow of AAAS, a corresponding member of the Academy of Sciences of Portugal, and a member of the US National Academy of Engineering.

Dr. Aylin Yener, Pennsylvania State University

Thursday, February 20, 2014 - 4:00pm - 5:00pm

Core Building Lecture Hall

Energy Harvesting Wireless Communication Networks

Abstract: Wireless communication networks composed of devices that can harvest energy from nature will lead to the green future of wireless, as energy harvesting offers the possibility of perpetual network operation without adverse effects on the environment. By developing effective and robust communication techniques to be used under energy harvesting conditions, some of the communication devices and networks can even be taken off the grid. Energy harvesting brings new considerations to system level design of wireless communication networks, leading to new insights. These include randomness and intermittency of available energy, as well as additional system issues to be concerned about such as energy storage capacity and processing complexity. The goal of this talk is to furnish the audience with fundamental design principles of energy harvesting wireless communication networks established in our recent work. The focus will be on identifying optimum transmission scheduling policies in various settings, the ensuing algorithmic solutions leading to design insights, and time permitting, some recent results on information theoretic limits.


Aylin Yener received the B.Sc. degree in electrical and electronics engineering, and the B.Sc. degree in physics, from Bogazici University, Istanbul, Turkey; and the M.S. and Ph.D. degrees in electrical and computer engineering from Wireless Information Network Laboratory (WINLAB), Rutgers University, New Brunswick, NJ. Commencing fall 2000, for three semesters, she was a P.C. Rossin endowed assistant professor at the Electrical Engineering and Computer Science Department, Lehigh University, PA. In 2002, she joined the faculty of The Pennsylvania State University, University Park, PA, where she was an assistant Professor, then associate Professor, and is currently professor of Electrical Engineering since 2010. During the academic year 2008-2009, she was a visiting associate professor with the Department of Electrical Engineering, Stanford University, CA. Her research interests are in wireless communications and networking, information theory, communication theory and network science, with recent emphasis on energy harvesting green communications and information security. She received the NSF CAREER award in 2003 and the Penn State Engineering Alumni Society Outstanding Research Award in 2010; and was a co-recipient of DARPA young investigator team award for the ITMANET program in 2006, and the best paper award in Communication Theory Symposium at the IEEE International Conference on Communications in 2010.

Dr. Yener has served as a technical program (co-)chair for various conferences including for the IEEE Communications Society (2008-2014). She was an associate editor for the IEEE Transactions on Communications (2009-2012), an associate editor and an editorial advisory board member for the IEEE Transactions on Wireless Communications (2001-2012). She served as the student committee chair for the IEEE Information Theory Society 2007-2011, and was the co-founder of the Annual School of Information Theory in North America in 2008. Dr. Yener is a member of the board of governors of the IEEE Information Theory Society and its treasurer since 2012.

Dr. Laura Fabris, Rutgers University, Dept. of Materials Science & Engineering

Wednesday, February 12, 2014 - 10:00am - 11:00am

CoRE Building Lecture Hall

Gold Nanoparticles for Imaging and Sensing


Gold nanoparticles have seen a widespread growth in application because of their many useful features, such as ease of synthesis, the stability, the non-cytotoxicity, but mainly because of their intriguing optical properties. The optical properties of gold nanoparticles are characterized by the coherent oscillation of their conduction electrons at the interface between the metal and the surrounding dielectric medium, and can give rise to intense absorption bands whose position mainly depends on the shape of the nanoparticle. One of the techniquesin which gold nanoparticles have found vast application is surface enhanced Raman spectroscopty (SERS), a non-linear near field technique that takes advantage of the intense electromagnetic field enhancement that takes place in close proximity to the surface of the nanoparticle. This enhancement is particularly intense at specific locations called "hot-spots". In this talk I will present our experiments aimed at increasing the SERS enhancement by carefully tailoring the hot spots.

First of all, I will describe our results in the preparation of multifunctional gold NP dimers capable of specific cell targeting, optimized uptake, and highly resolved cel imaging that outperforms fluorescence. The systems presented here will be based on spherical NP dimers targeting U87 glioblastoma cells. Dithiolated linkers have been used to lock the NPs in a dimer conformation and also act as SERS reporters. In our experiments, thiolated PEG was used to stabilize the NP systems, while cyclic RGD peptides were employed to target avb3 integrins overexpressed on the surface of cancerous cells. Combined analysis carried out via fluorescence and Raman microscopy demonstrated the efficient SERS=based imaging of U87 cells that outperformed fluorescence.

In a second set of results, I will describe the use of gold nanostars. Gold nanostars are nanoparticles with a spherical core and very sharp spikes, who sharpness can be modulated by tuning the synthetic parameters. In this work, we have synthesized gold nanostars and deposited them on a gold substrate via a bifunctional tether, hence locking them on the surface. The sharp spikes of the nanostars act as excellent hot spots enabling the use of these substrates for SERS-based chemical sensing. We have demonstrated that this sensor is able to selectively identify both chemisorbed and physisorbed analytes down to the femtomolar regime with high selectivity and multiplexing ability.


Dr. Fabris earned her B.S./M.S. in Physical Chemistry at the University of Padova, Italy. After a one-year experience in industry she returned to graduate school to pursue her Doctorate Degree in Chemical Sciences, always at the University of Padova. Her dissertation work focused on the synthesis and characterization of peptide protected gold nanoclusters. She then took a postdoctoral position in the Department of Chemistry and Biochemistry at the University of California at Santa Barbara, where she remained until June 2009. Her work entailsed the development of surface enhanced Raman scattering (SERS)-based biosensors. In July 2009 she joined the Department of Materials Science and Engineering, where is is currently an Assistant Professor. Dr. Fabris is also a Graduate Faculty member of the Departments of Chemistry and Chemical Biology and Biomedical Engineering at Rutgers. Her research is targeting the study of plasmonic nanoparticles and their application as tags for SERS-based cell imaging and for the efficiency enhancement of plasmonic organic solar cells.

Dr. Bijan Pesaran, New York University

Wednesday, December 4, 2013 - 10:00am - 12:00pm

CoRE Lecture Hall


The study of the brain is enjoying an era of growth with dramatic advances in our knowledge of the link between brain and behavior. Research is leading to a better scientific understanding of how the brain controls behavior and is opening up translational opportunities to engineer devices that replace lost brain function. Our understanding of brain mechanisms is largely based on the spiking activity of individual neurons. In this talk, I will argue that an exclusive focus on spiking activity hampers both basic neuroscience and neural engineering. I will develop a complementary approach involving local field potentials (LFPs), electrical potentials generated by populations of neurons. I propose that LFPs show promise in two specific areas. Local field potentials can improve our scientific understanding of how different brain areas communicate with each other during behavior and can accelerate the development of robust high-performance neural interfaces that replace lost brain function.


Bijan Pesaran completed his undergraduate training in Physics at the University of Cambridge, UK. He went on to earn his PhD in Physics at the California Institute of Technology in 2002. He then completed postdoctoral research in Biology until 2005. He has been on the faculty at New York University since 2006 where he is currently Associate Professor of Neural Science in the Center for Neural Science. Among his other honors he has received a Sloan Research Fellowship, a McKnight Scholar Award and an NSF CAREER Award.

Dr. K. J. Ray Liu,
Christine Kim Eminent Professor of Information Technology,
Department of Electrical and Computer Engineering,
University of Maryland, College Park

Wednesday, November 20, 2013 - 10:00am - 12:00pm

CoRE Building Lecture Hall

Title:   A Time-Reversal Paradigm for Green Internet of Things


In recent years, with the explosive growth of wireless communication, the energy consumption of wireless networks and devices is experiencing a dramatic increase. Because of ubiquity of wireless applications, such increasing energy consumption not only results in a high operational cost and an urgent demand for battery/energy capacity to wireless communications operators, but also causes a more severe electromagnetic pollution to the global environment. Therefore, an emerging concept of "Green Communications" has received considerable attention in hope of finding novel solutions to improve energy efficiency, relieve/reduce radio pollution to unintended users, and maintain/improve performance metrics.

To qualify as a green wireless technology, one must meet two basic requirements: one is low energy consumption (environmental concerns) and the other is low radio pollution to others (health concerns) besides the intended transmitter and receiver. In the first part of the talk, we argue and show that the time-reversal (TR) signal transmission is an ideal paradigm for green wireless communications because of its inherent nature to fully harvest energy from the surrounding environment by exploiting the multi-path propagation to re-collect all the signal energy that would have otherwise been lost in most existing communication paradigms. Our theoretical analysis and simulations show that a potential of over an order of magnitude of power reduction and interference alleviation can be achieved. We also demonstrate a very high multi-path diversity gain exhibiting in a TR system. In essence, TR transmission treats each multi-path as a virtual antenna and makes full use of all the multi-paths. Experimental results obtained from measurements in real RF multi-path environment are shown to demonstrate the great potential of TR-based transmission as an energy-efficient green wireless communication paradigm. In the second part, we will demonstrate why the TR paradigm is an ideal technology for the future green Internet of Things.


Dr. K. J. Ray Liu was named a Distinguished Scholar-Teacher of University of Maryland in 2007, where he is Christine Kim Eminent Professor of Information Technology. He leads the Maryland Signals and Information Group conducting research encompassing broad areas of signal processing and communications with recent focus on cooperative communications, cognitive networking, social learning and networks, and information forensics and security. Dr. Liu has received numerous honors and awards including IEEE Signal Processing Society 2009 Technical Achievement Award. A Fellow of the IEEE and AAAS, he is recognized by Thomson Reuters as an ISI Highly Cited Researcher. Dr. Liu is President of IEEE Signal Processing Society. He was the Editor-in-Chief of IEEE Signal Processing Magazine and the founding Editor-in-Chief of EURASIP Journal on Advances in Signal Processing. Dr. Liu also received various research and teaching recognitions from the University of Maryland, including Poole and Kent Senior Faculty Teaching Award, Outstanding Faculty Research Award, and Outstanding Faculty Service Award, all from A. James Clark School of Engineering; and Invention of the Year Award from Office of Technology Commercialization.

Dr. Chao Tian, AT&T Research

Wednesday, November 13, 2013 - 10:00am - 12:00pm

CoRE Building Lecture Hall

Title: New Results on Regenerating Codes for Distributed Data Storage

Abstract: In large data centers and peer-to-peer data storage systems, traditional erasure codes such as Reed-Solomon codes incur a high repair cost when a node fails, and thus new repair-efficient codes are urgently needed. Despite extensive recent efforts, it remains an open problem whether the cut-set outer bound can indeed be achieved. In the first part of the talk I provide an answer (in the negative) to this problem by completely characterizing the rate-region of the code under a specific set of parameters. The main difficulty in establishing this result is the converse part, and the traditional manual proof approach used by information theorists for the past 60 years appears quite infeasible here. A computer-aided proof (CAP) approach is thus developed, by taking into account the symmetry in the problem, and moreover utilizing the linear programming duality, to yield an explicit algebraic proof. In the second part of the talk, I briefly discuss a new code construction based on layered error correction and combinatorial block designs, which can achieve new operating points that are not possible using existing constructions.

Bio: Dr. Tian received the B.E. degree in Electronic Engineering from Tsinghua University, Beijing, China, in 2000 and the M.S. and Ph. D. degrees in Electrical and Computer Engineering from Cornell University, Ithaca, NY in 2003 and 2005, respectively. He was a postdoctoral researcher at Swiss Federal Institute of Technology at Lausanne (EPFL) from 2005 to 2007, and then joined AT&T Labs-Research, Florham Park, New Jersey. Dr. Tian received the Liu-Memorial Award at Cornell University in 2004, and AT&T Key Contributor Award in 2010 and 2011. He is an associate editor of the IEEE Signal Processing Letters, and an adjunct associate professor at Columbia University.

Dave Wilson, Director of Academic Programs, National Instruments

Thursday, November 7, 2013 - 11:00am - 12:00pm

CoRE Bldg Board Room, Room 701

Title: Engineering the Future using Systems Design

How do students prepare to engineer the future? How will expectations for higher level functionality change the knowledge requirements of engineers? This presentation will explore the spectrum of components and skills that engineering students need to possess when they graduate. We will tour the fundamental components of electronics, mechanics and programming. And then look at the ways to efficiently combine them into systems that will serve to meet the most challenging demands on science and engineering. With NI's vast array of research and industry applications, we will take a close look at some advanced applications that are redefining capabilities and resetting expectations on what can be accomplished. And, most importantly, we~Rll comment on paths students are taking to be prepared to participate immediately upon graduating and enter the world ready to "Do Engineering".


As the Director of Training and Academic Programs for National Instruments, Dave Wilson works with the both NI headquarters and more than 45 NI branches around the globe. He ensures the most effective product proficiency development strategies and tactics are implemented worldwide.

Before joining NI, Dave worked for the Xerox Corporation and Keithley Instruments as a research engineer and software developer. Upon joining NI in 1991 as a Michigan-area district sales manager, he began driving the adoption of NI measurement and automation solutions throughout the automotive industry. In this role, he presented more than 50 technical seminars, wrote hundreds of applications with customers, and received multiple industry recognition awards.

In 1995, Wilson became the director of data acquisition marketing where he led several successful launches for products that have become key parts of the NI product line including motion control, Vision, DAQ boards, and PXI. He also developed product and corporate messages and led initiatives to work with R&D to incorporate customer-recommended features into new products. In 2000, Wilson became the international sales director for NI Japan where he led the branch to record growth.

Wilson has delivered more than 60 keynotes about the application of next-generation technologies in 30 countries in Asia, Europe and the Americas. He has met with the ministers of education in both Russia and Kosovo and many Deans of engineering to discuss ways to adopt new generation technologies for science and engineering in university curricula. He has also authored numerous articles and interviewed with multiple domestic and international publications including EE Times Asia, Bits & Chips, Evaluation Engineering, Desktop Engineering, and Sensors.

Additionally, Wilson has chaired the most successful customer recognition event held by NI, the Graphical System Design Achievement Awards. For ten years, this event has recognized NI customers around the world for accomplishments in engineering and science.

Wilson holds a bachelor of science degree in applied physics from the State University of New York.

Dr. Steven McLaughlin, Georgia Tech

Wednesday, November 6, 2013 - 10:00am - 12:00pm

CoRE Building Lecture Hall

Dr. Peter Ramadge, Princeton University

Wednesday, October 30, 2013 - 10:00am - 12:00pm

CoRE Building Lecture Hall


The sparse representation of signals with respect to an over-complete dictionary has been of recent interest in a broad range of applications. One of the most used methods for obtaining sparse codes, the Lasso problem, becomes computationally costly for large dictionaries and this hinders the use of this approach for large-scale decision tasks. Recently, dictionary screening has been used to address this computational issue.

In this spirit, this talk shows how sequential Lasso screening can also facilitate faster completion of sparse representation decision tasks, such as classification, without a major impact on statistical accuracy. The sequential screening process allows us to employ an early decision mechanism that can accelerate classification, possibly at the cost of small decrease in accuracy. The talk will discuss the theoretical background that underlies these methods and demonstrate results on several classification tasks. In particular, for clip-level music genre classification, using scattering features and a new voting scheme, we show that the proposed method yields improved clip classification accuracy and considerable computational speedup.


Dr. Peter Ramadge received the B.Sc. and B.E. degrees and the M.E. degree from the University of Newcastle, Australia, and the Ph.D. degree from the Department of Electrical Engineering at the University of Toronto, Canada. He joined the faculty of Princeton University in September 1984, where he is currently Gordon Y.S. Wu Professor of Engineering, and Professor of Electrical Engineering.

Dr. Ramadge has been a visiting Professor at the Massachusetts Institute of Technology and a Visiting Research Scientist at IBM's Tokyo Research Laboratory. He is a Fellow of the IEEE and a member of SIAM. He has received several honors and awards including: a paper selected for inclusion in IEEE book "Control Theory: Twenty Five Seminal Papers (1932-1981)", an Outstanding Paper Award from the Control Systems Society of the IEEE and is listed in His current research is in the domain of statistical signal processing and machine learning, and fMRI data analysis.

Andrew Grimshaw, University of Virginia

Wednesday, October 23, 2013 - 1:00pm - 2:00pm

CoRE Building Lecture Hall


Lowering the barriers to collaboration and increasing access to high-end resources will accelerate the pace and productivity of science and engineering. Toward this end, the eXtreme Science and Engineering Discovery Environment (XSEDE) is a single virtual system that allows scientists to seamlessly and interactively share computing resources, data, and expertise. The XSEDE project will allow researches to link and access resources at both domestic and foreign supercomputing centers as well as resources belonging to university campuses and research labs around the world. The complexity of distributed systems creates obstacles for scientists who wish to share their resources with collaborators. Obstacles include: complex, unreliable, and unfamiliar tools and environments; multiple administrative domains each with their own passwords and file systems; the need to keep track of which resources are on which machines; the need to manually copy files and applications from place to place; the need to monitor and interact with multiple execution services, each with their own idiosyncratic behavior; and the need to manage authorization, identities and groups. The best way to manage complexity and make sharing data and resources possible on a large scale to provide users with a familiar, easy-to-use tool that manages aspects of the collaboration on the user’s behalf. The first principle of XSEDE’s approach to designing a collaborative interface is familiarity: give the user interaction paradigms and too ls that are similar to those she already uses. XSEDE deploys what it calls the Global Federated File System (GFFS) in order to leverage the user’s familiarity with the directory-based paradigm.

The GFFS is a global shared namespace designed so that the user can easily organize and interact with files, execution engines, identity services, running jobs, and much more. Many types of resources, such as compute clusters, directory trees in local file systems, and storage resources, can be linked into the GFFS
directory structure by resource owners at centers, on campuses, and in individual research labs. GFFS resources can be accessed (subject to access control) in a variety of ways: from the command line (useful for scripting); via a GUI; or by being mapped directly into the local file system. When mapped into the local file system, remote resources can be accessed by existing applications as if they were local resources. In this talk I will present the GFFS, its functionality, its motivation, as well as typical use cases. I will demonstrate many of its capabilities, including: how to secure shared data with collaborators; how to share storage with collaborators; how to access data at the centers from campus and vice versa; how to create shared compute queues with collaborators who can then schedule jobs on collaboration “owned” resources; how to create jobs and how to interact with them once started. I will present the GFFS’s various access mechanisms, i.e., the GUI and local file system mapping; if facilities permit, I will include this latter mechanism in the live demonstration.


Dr. Andrew Grimshaw received his Ph.D. from the University of Illinois at Urbana-Champaign in 1988. He joined the University of Virginia as an Assistant Professor of Computer Science, becoming Associate Professor in 1994 and Professor in 1999. He is the chief designer and architect of Mentat, Legion, Genesis II, and the co-architect for XSEDE. In 1999 he co-founded Avaki Corporation, and served as its Chairman and Chief Technical Officer until 2003. In 2003 he won the Frost and Sullivan Technology Innovation Award. In 2008 he became the founding director of the University of Virginia Alliance for Computational Science and Engineering (UVACSE). The mission of UVACSE is to change the culture of computation at the University of Virginia and to accelerate computationally oriented research.

Andrew is a leading member of the Open Grid Forum (OGF), serving both as a member of the OGF's Board of Directors and as Architecture Area Director. Andrew is the author or co-author of over 50 publications and book chapters. His current projects are Genesis II and XSEDE. Genesis II, is an opensource, standards-based, Grid system that focuses on making Grids easy-to-use and accessible to non computer-scientists. XSEDE (eXtreme Science and Engineering Discovery Environment) is the NSF follow-on to the TeraGrid project.

Dr. Judy Qiu, School of Informatics and Computing, Indiana University at Bloomington

Friday, October 18, 2013 - 10:00am - 12:00pm

CoRE Lecture Hall


Many scientific applications are data intensive. It is estimated that organizations with high- end computing infrastructures and data centers are doubling the amount of data that they are archiving every year. Twister extends MapReduce, enabling HPC-Cloud Interoperability. We show how to apply Twister to support large-scale iterative computations that are common in many important data mining and machine learning applications. Furthermore, one needs additional communication patterns from those familiar in MapReduce. This leads us to the new Map-Collective programming model which we develop here based on requirements of a range of applications by extending our existing data analysis framework Twister. The resultant Map-Collective model, which captures the full range of traditional MapReduce and MPI features, is built on a new communication abstraction. It will be integrated with Hadoop and evaluated with Twister, HDinsight and Twister4Azure. Our work includes a detailed performance evaluation on IaaS or HPC environments such as Azure, FutureGrid and IU’s Big Red II supercomputer, and provides useful insights to both frameworks and applications.


Dr. Judy Qiu is an Assistant Professor of Computer Science in the School of Informatics and Computing at Indiana University and an assistant director of the school’s Digital Science Center. Her research interests are parallel and distributed systems, cloud computing, and high-performance computing. Qiu leads the SALSA project, involving professional staff and Ph.D. students from the School of Informatics and Computing. SALSA focuses on data-intensive computing at the intersection of cloud and multicore technologies with an emphasis on scientific data analysis applications by using MapReduce and traditional parallel computing approaches. Her research has been funded by NSF, NIH, Microsoft, Google and Indiana University. She is a recipient of NSF CAREER Award in 2012

Dr. William Pence, WebMD

Wednesday, October 16, 2013 - 10:00am - 12:00pm

CoRE Building Lecture Hall

Title:   WebMD Mobile: Overview of WebMD Mobile Services and Opportunities for Users and Advertisers


WebMD is the world's largest mobile health information provider, with more than 24M monthly unique users on their consumer mobile web site alone. WebMD has also introduced a compelling line of mobile apps for consumer and health care providers, and more recently has started to introduce features that allow physicians and consumers to connect, for users to create more personalized experiences, and for new forms of data to be captured and leveraged.

In this talk I will provide an overview of WebMD's scale and lines of business, describe new ways that WebMD is leveraging data to create more personalized user experiences and enable novel advertising and sponsorship messages, and new apps that enable a future world of connected wearable devices and digital connections between consumers and their health care providers and medical data.


Executive Vice President, Chief Operating Officer and Chief Technology Officer William Pence has served as Executive Vice President and Chief Technology Officer of WebMD since November 2007 and has also served Chief Operating Officer of WebMD since May 2012. Before joining WebMD, Mr. Pence had served as Chief Technology Officer and Senior Vice President at Napster since 2003. From 2000 to 2003, Mr. Pence was the Chief Technology Officer for Universal Music Group's online initiatives and for the pressplay joint venture with Sony. That joint venture later served as the basis for the relaunched Napster service. Previously, Mr. Pence spent more than a decade at IBM, where he held various technology management positions in research as well as in the software division, focused on guiding research and development and commercializing technology for IBM product divisions. Mr. Pence received a Bachelor of Science in Physics from the University of Virginia, and a Ph.D. in Electrical Engineering from Cornell University.

Dr. Frank Bentley, Yahoo! Research

Wednesday, October 9, 2013 - 10:00am - 12:00pm

CoRE Building Lecture Hall


Health Mashups: Encouraging behavior change through identifying long-term patterns between wellbeing data and context


People now have access to many sources of data about their health and wellbeing. Yet, most people cannot wade thr-ough all of this data to answer basic questions about their long-term wellbeing: Do I gain weight when I have busy days? Do I walk more when I work in the city office? Do I sleep better on nights after I work out?

We built the Health Mashups system to identify connections that are significant over time between weight, sleep, step count, calendar data, location, weather, pain, food intake, and mood. These significant observations are displayed in a mobile application using natural language, e.g. "You are happier on days when you sleep more." We performed a pilot study, made improvements to the system, and then conducted a 90-day trial with 60 diverse participants, learning that interactions between wellbeing and context are highly individual and that our system supported an increased self-understanding that lead to focused behavior changes.


Frank Bentley is a Principal Research Scientist in Mobile Sensing and User Behavior Research at Yahoo! Labs in California. Frank's work investigates the ways in which mobile devices can help strengthen strong-tie social relationships. He takes projects from early conceptual studies through to prototyping, field evaluation, and product as a routine process. Frank recently joined Yahoo! after 11 years at Motorola Labs in Chicago. He also teaches a Mobile HCI class at MIT and recently completed a book, Building Mobile Experiences, with MIT Press.

Dr. Selin Aviyente, Michigan State University

Wednesday, September 25, 2013 - 11:00am - 12:00pm

CoRE Building Lecture Hall

Title: A Signal Processing Framework for Studying Dynamic Functional Brain Networks

Abstract: Increasingly sophisticated neuroimaging methods have opened up important areas of basic research in psychiatry, psychology, and neurology. These neuroimaging modalities pose new challenges and opportunities for the signal processing community to analyze highly complex, multi-dimensional and dynamic data. One particular challenge is the identification of dynamic functional networks underlying observed neural activity. Current imaging modalities index local neural activity very well, but there is an increasing need for methods that provide measures of the interaction between regional activations. In this talk, I will focus on signal processing methods to quantify this interaction between different brain regions based on the electroencephalogram (EEG) measurement of the brain activity. In the first part of the talk, I will present phase synchronization as a plausible mechanism for modeling the reciprocal interactions between local networks of the brain. In order to quantify the time varying nature of interactions through phase synchronization, a new time-frequency phase synchrony (TFPS) measure based on Rihaczek distribution will be introduced. Graph theoretic methods to characterize the topology of functional connectivity networks constructed by this new synchrony measure will be discussed. In the second part of the talk, I will present subspace analysis based methods for extracting information from time-varying functional connectivity networks. Finally, application of the proposed methods to EEG data containing the error-related negativity (ERN), a brain potential response that indexes endogenous action monitoring, will be presented.


Selin Aviyente received her B.S. degree with high honors in electrical and electronics engineering from Bogazici University, Istanbul in 1997. She received her M.S. and Ph.D. degrees, both in Electrical Engineering: Systems, from the University of Michigan, Ann Arbor, in 1999 and 2002, respectively. Currently, she is an associate professor in the Department of Electrical and Computer Engineering at Michigan State University. Her research focuses on statistical signal processing, in particular non-stationary signal analysis, with applications to biological signals. Her most recent work focuses on the study of the functional networks in the brain. She is the recipient of 2005 Withrow Teaching Excellence Award and 2008 NSF CAREER Award.

Prof. Janne Lindqvist, Department of Electrical & Computer Engineering, Rutgers University

Wednesday, September 11, 2013 - 10:00am - 12:00pm

CAIT Auditorium 100 Brett Road

Title:Nudging People with Computer Systems

Computer systems today affect directly or indirectly billions of people. For example, using mobile phones directly integrates computer systems into people’s daily lives. In this talk, I will present my research program on redesigning computer systems for detecting and nudging behavior change. We will discuss how to redesign mobile phone platforms to make privacy-sensitive sensor access (e.g. localization) transparent to users, and how this affects their behavior. We will also discuss how similar approaches can be used for other important societal purposes including password security, mitigating distracted driving and bringing people together in local communities.

Janne Lindqvist is an assistant professor of Electrical and Computer Engineering and a member of WINLAB at Rutgers University. Janne leads the Rutgers Human-Computer Interaction group. From 2011-2013, Janne was an assistant research professor of ECE at Rutgers.

Prior to Rutgers, Janne was a post-doc with the Human-Computer Interaction Institute at Carnegie Mellon University’s School of Computer Science. Janne received his M.Sc. degree in 2005, and D.Sc. degree in 2009, both in Computer Science and Engineering from Helsinki University of Technology, Finland. He works at the intersection of human-computer interaction, mobile computing and security engineering. Before joining academia, Janne co-founded a wireless networks company, Radionet, which was represented in 24 countries before being sold to Florida-based Airspan Networks in 2005. His work has been featured several times in MIT Technology Review and recently also in New York Times,, Tech Republic, and other online venues. During his first year at Rutgers, Janne was awarded three NSF grants totaling nearly $1.3 million and a MobiCom best paper award.

Dr. Stephen Hughes, Queens University, Canada

Tuesday, April 30, 2013 - 10:00am - 12:00pm

CoRE Building Lecture Hall

Dr. Alicia Abella, AT&T Labs Research

Wednesday, April 24, 2013 - 1:00pm - 2:00pm

CoRE Building Lecture Hall

Title: Empowering Devices


When I think back, at a device and its accompanying service that truly gave people an unexpected new ability I can¹t help but think back to the invention of the telephone itself. When I think about what people had to do prior to that to talk to their loved ones or friends, they basically had to get on a boat, a horse, or walk for miles. Not only was this long, it was dangerous. The telephone made it possible for people to talk to other people from the comfort of their home. Today we can not only hear each other but see each other too. Whether your across the street or around the globe. In this talk I will review a research program at AT&T Labs - Research called Empowering Devices. The vision of this research work is to create novel devices and companion services that will exploit the wireless and wired network to stimulate demand, while exciting people with unexpected new abilities.


Dr. Abella is Executive Director of the Innovative Devices and Services Research Department at AT&T Labs, where she manages a group of multi-disciplinary technical staff specializing in human-computer interaction, mobile services, SIP/VoIP technology, ubiquitous computing, and emerging devices.

In May 2013, Dr. Abella will receive Columbia University’s Medal of Excellence, an award given each year to an alumnus or alumna, under 45 years of age, whose record in scholarship, public service, or professional life is outstanding. This is the first time since 1929 -- when the award was first given -- that Columbia has awarded the medal to an engineer. In 2011, she was selected by President Obama to be on his Presidential Advisory Commission for Educational Excellence for Hispanics. Also in 2011, she was inducted into the prestigious WITI(Women in Technology International) Hall of Fame. In 2010, she was honored as one of the Top Five Women of the Year by Hispanic Business Magazine. She is also a member of the elite group of AT&T Science and Technology Medal award winners and recipient of the Hispanic Engineers National Achievement Award for Outstanding Technical Achievement.

Besides her technical contributions, Dr. Abella has been a strong advocate infostering the development of minorities and women in science and engineering. As Executive Vice President for the Young Science Achievers program,a program she has been involved with for 9 years, she has worked tirelessly to bring an interest and excitement in science and engineering to high school aged women and minority students through a program of mentoring and scientific achievement.

Following completion of her Ph.D. in Computer Science from Columbia University in 1995, Dr. Abella joined AT&T Bell Laboratories in Murray Hill, NJ where she began her work in the problem of natural language understanding and dialog management. In addition to her doctoral work, Dr. Abella received a MS from Columbia and BS from NYU, both in Computer Science. She lives in Morristown, New Jersey, with her husband Alex and their son Mark.

Dr. Mohammad Ali Maddah-Ali, Bell Labs, Alcatel-Lucent

Wednesday, April 17, 2013 - 10:00am - 12:00pm

CoRE Building Lecture Hall

Title: Fundamental Limits of Caching


Caching is a technique to reduce peak traffic rates by prefetching popular content in memories at the end users. Conventionally, cache memories are exploited by delivering requested contents in part locally rather than through the network. In this talk, we present a novel caching approach that can achieve a significantly larger reduction in peak rate compared to previously known caching schemes. In particular, the improvement can be on the order of the number of end users in the network. In the proposed approach, the cache placement is carefully designed in order to create multicasting opportunities even among users with different demands. Using an information theoretic argument, we show that the proposed scheme is within a constant factor from "capacity" for all values of the problem parameters.

This is a joint work with Urs Niesen.


Mohammad Ali Maddah-Ali received the PhD from the University of Waterloo, Waterloo, ON, Canada. From March 2007 to December 2007, he was working at the Wireless Technology Laboratories, Nortel Networks, Ottawa, ON, Canada, in a joint program between University of Waterloo and Nortel Networks. From January 2008 to August 2010, he was a Postdoctoral Fellow at the Wireless Foundations Center, Department of Electrical Engineering and Computer Sciences, the University of California at Berkeley. Since September 2010, he has been with Bell Laboratories, Alcatel-Lucent, Holmdel, NJ, as a communication networks research scientist. His research interests include wireless communications and network information theory.

Dr. Ananya Sen Gupta, University of Iowa

Wednesday, April 10, 2013 - 10:00am - 11:00am

CoRE Building Lecture Hall

Title: "Studying the environment as a complex system: breaking the barrier of traditional assumptions"


Traditional signal processing approaches are often built on well-known assumptions such as linearity, time-invariance, and incoherence among the signals involved. In reality, many of these assumptions are violated in physical environments and therefore, conventional techniques fail to solve signal processing issues effectively in real-world systems. For example, a classic challenge to quantifying the environmental impact of a major oil spill such as the 2010 Macondo well spill in the Gulf of Mexico lies in distinguishing its fingerprint against closely correlated natural seeps and other reservoirs in the area. Multi-user detection in the presence of high correlation and amplifier saturation, tracking the time- varying delay spread of shallow water acoustic channels, sonar detection against severe clutter, among many other applications also face similar challenges.

My talk will focus on some of these problems encountered in challenging physical environments, such as the ocean, as they relate my research in adaptive signal processing, sparse optimization and signal separation techniques. While the techniques I will discuss have been developed with target applications in mind, the scope of signal processing innovations go beyond specific applications to the broader paradigm of other well-known problems with similar issues. In particular, I will present my vision on how breaking the barrier of traditional assumptions and constraints can enable the scientific study of the environment as a diverse, complex and dynamic system, with the end goal of enhancing better balance between anthropogenic activities and environmental health and sustenance.


Dr. Ananya Sen Gupta got her MS (Aug. 2001) and PhD (Dec. 2006) in Electrical Engineering from the University of Illinois at Urbana-Champaign. Her broad area of expertise is signal processing, particularly interference mitigation, channel estimation and sparse optimization techniques. Throughout her research career Dr. Sen Gupta has been interested in seeking novel solutions for signal processing challenges commonly encountered in real environments. Her Master's research was in dynamic MRI imaging under the supervision of Dr. Zh-Pei Liang, and her doctoral research was in non-linear multi-user interference suppression under the supervision of Dr. Andrew Singer. Aug 2008-2012 she has been a postdoctoral researcher at the Woods Hole Oceanographic Institution under the supervision of Dr. James Preisig, working in shallow water acoustic channel tracking using adaptive sparse optimization techniques. As her continuing position at WHOI, she has been actively collaborating with Dr. Christopher Reddy to develop novel informational methods that provide collective forensic interpretation of petroleum samples found in the ocean using comprehensive two-dimensional gas chromatography. More recently, she has joined the University of Iowa, Department of Electrical and Computer Engineering, as tenure-track faculty. Her active research interests involve signal processing challenges commonly encountered in complex environments, with particular focus on fingerprinting and sparse optimization across a variety of applications.

Dr. Jie Liu, Microsoft Research

Monday, April 8, 2013 - 10:00am - 12:00pm

CoRE Building Lecture Hall

Title: "A Fresh Look at Mobile Location Sensing"


Location-based services have become ubiquitous thank to sensors like GPS and WiFi in our smart phones and other mobile devices. However, continuous location sensing such as logging, tracking, and geo-fencing, consume too much energy and shorten device battery life. In this talk, we take a fresh look at location sensing, in both outdoor and indoor settings. For outdoor location, we dive into the principles of GPS receivers and show that by offloading GPS processing to the cloud, we can reduce the device side energy consumption by three orders of magnitude. For indoor location, we discover that commercial FM signals are good sources of location signatures that work better than WiFi signatures by themself, and works even better if combined with WiFi signatures. These low energy alternatives enable always-there location services without users paying battery life penalty.

Presentation Slides of Dr. Liu's talk
The Presentation slides (pdf) of Dr. Jie Liu's talk entitled "A Fresh Look at Mobile Location Sensing" are located   here.


Dr. Jie Liu is a Principal Researcher at Microsoft Research, Redmond, WA, and the manager of its Sensing and Energy Research Group. His research interests root in understanding and managing the physical properties of computing. Examples include timing, location, energy, and the awareness of and impact on the physical world. He has published broadly in areas like sensor networks, embedded systems, ubiquitous computing, and energy efficient cloud computing. Dr. Liu is an Associate Editor of ACM Trans. on Sensor Networks, has been an Associate Editor of the IEEE Trans. on Mobile Computing, and has chaired a number of top tier conferences. He is an ACM Distinguished Scientist. Dr. Liu received his Ph.D. degree from Electrical Engineering and Computer Sciences, UC Berkeley in 2001. From 2001 to 2004, he was a research scientist at Palo Alto Research Center (formerly Xerox PARC).

Prof. Ilan Lobel
Stern School of Business
New York University

Wednesday, April 3, 2013 - 10:00am - 11:00am

CoRE Building Lecture Hall

Title: "Inter-temporal Price Discrimination: Structure and Computation of Optimal Policies"


We consider the question of how should a firm optimally set a sequence of prices in order to maximize its long-term average revenue given a continuous flow of strategic customers. In particular, customers arrive over time, are strategic in timing their purchases and are heterogeneous along two dimensions: their valuation for the firm's product and their willingness to wait before purchasing or leaving. The customers' patience and valuation may be correlated in an arbitrary fashion. For this general formulation, we prove that the firm may restrict attention to short cyclic pricing policies, which have length twice the maximum willingness to wait of the customer population. We further establish results on the subpotimality of monotone pricing policies in general, and illustrate the structure of optimal policies. These are, in a typical scenario, characterized by nested sales, where the firm offers partial discounts throughout each cycle, offers a significant discount halfway through the cycle, with the largest discount offered at the end of the cycle. From a computational perspective, we exploit the structure of the underlying problem to develop a novel dynamic programming formulation for the problem that computes an optimal pricing policy in polynomial time (in the maximum willingness-to-wait). We further establish a form of equivalence between the problem of pricing for a stream of heterogeneous strategic customers and pricing for a pool of heterogeneous customers who may stockpile units of the product. Joint work with Omar Besbes (Columbia).

The paper is available here:


Dr. Ilan Lobel is an Assistant Professor of Information, Operations and Management Sciences at New York University's Stern School of Business. Prior to joining NYU Stern in 2010, he was a post-doctoral researcher at the Microsoft Research New England Lab. He received his Ph.D. in Operations Research from the Massachusetts Institute of Technology in 2009 and his B.Sc. in Electrical Engineering from the Pontificia Universidade Catolica of Rio de Janeiro in 2004. Professor Lobel's research focuses on the operations of Internet-based businesses, including issues such as pricing, learning and contract design in online, dynamic and networked markets. His work focuses on application domains such as online advertisement, cloud computing and social networks.

Dr. Chang-Hwan Choi
Steven's Institute of Technology

Wednesday, March 27, 2013 - 1:00pm - 2:00pm

CoRE Building Lecture Hall

Dr. William Arnold
Chief Scientist and Vice President of Technology Development Center

Monday, March 11, 2013 - 11:00am - 12:00pm

CoRE Building Lecture Hall


Lithography is used to pattern semiconductor devices such as memories and processors. Water immersion lithography using the ArF excimer laser source at 193nm is the key manufacturing tool today. EUV lithography is developed to continue the shrink of integrated circuit features below 20nm half pitch.

Laser produced plasma sources operating at 13.5nm are used with all-reflective optics to expose resists. This presentation will describe the status of EUVL and the remaining challenges to bring it into high volume manufacturing by 2015.


Bill Arnold is for 15 years the chief scientist of ASML, the world's leading supplier of lithography equipment for semiconductor manufacturing. He is also Sr. Vice President of the ASML Technology Development Center. Before that he was 18 years with Advanced Micro Devices, a leading manufacturer of microprocessors. He had many jobs over time at AMD, the last being Director of Advanced Process Development and Sr. Fellow. In 2013, Mr. Arnold is the President of SPIE, the International Society for Optics and Photonics.

Dr. Demetrios Christodoulides
CREOL-College of Optics and Photonics
University of Central Florida

Thursday, March 7, 2013 - 10:00am - 11:00am

CoRE Building Room 701 Boardroom

Title:    Exploring similarities between classical and quantum systems


Analogies between different disciplines provide a powerful tool in understanding nature. As such, quantum-classical optical similarities offer new opportunities in manipulating classical optical fields or quantum states. In recent years, many of the ramifications of these concepts have come to fruition on several fronts in the general area of optics. What made this possible are some new advances in structure fabrication and beam synthesis techniques. In this talk, we will provide an overview of our activities in this field. As an example, we will consider accelerating optical wavepackets in the form Airy beams as a means to bend light for applications in plasmonics, extreme nonlinear optics, and biology. Studying quantum inspired phenomena in artificial optical structures that would have been otherwise impossible to directly observe in their own habitat, like Anderson localization, parity-time (PT) symmetry, Bloch oscillations, Klein tunneling, etc. will be discussed. Finally, the possibility of quantum state engineering in periodic and random optical lattices will be reviewed in this talk.


Demetri Christodoulides is a Provost’s Distinguished Research Professor at CREOL-the College of Optics and Photonics of the University of Central Florida. He received his Ph.D. degree from Johns Hopkins University in 1986 and he subsequently joined Bellcore as a post-doctoral fellow at Murray Hill. Between 1988 and 2002 he was with the faculty of the Department of Electrical Engineering at Lehigh University. His research interests include linear and nonlinear optical beam interactions, synthetic optical materials, optical solitons, and quantum electronics. He has authored and co-authored more than 250 papers. He is a Fellow of the Optical Society of America and the American Physical Society. In 2011 he received the R.W. Wood Prize of OSA.

Dr. Michela Taufer
University of Delaware

Wednesday, March 6, 2013 - 10:00am - 11:00am

CoRE Building Lecture Hall

Title: Transforming computing algorithms and paradigms in HPC to enable more science out of our day-to-day simulations


My research group at the University of Delaware is engaged in interdisciplinary research with scientists and engineers in fields such as chemistry, chemical engineering, pharmaceutical sciences, seismology, and mathematics. The group's mission is to increase scientific discovery by transforming the way scientific applications deploy high performance computing (HPC) including distributed and multi-core systems. Several research vignettes will be presented that show how we have been successful in addressing challenges in science such as classifying protein-ligand binding geometries of billions of ligands in molecular docking in linear time, performing accurate simulations of fully atomistic macromolecular systems at meso-length and time scales, and improving numerical reproducibility and stability of MD simulations on multi-core platforms.


Michela Taufer joined the University of Delaware in 2007 as an assistant professor. She was promoted to associate professor with tenure in 2012. In January 2013 she was named the David L. and Beverly J.C. Mills Chair of Computer and Information Sciences. She earned her master's degrees in Computer Engineering from the University of Padova (Italy) and her doctoral degree in Computer Science from the Swiss Federal Institute of Technology (Switzerland). Taufer completed her postdoctoral studies as a La Jolla Interfaces in Sciences Fellow at the Center for Theoretical Biological Physics (CTBP), at the University of California, San Diego. Taufer's research focuses on efficient computational algorithms and adaptive scheduling policies for scientific computing on HPC platforms including multi-core, cloud, and volunteer computing.

Dr. Nicolas Madamopoulos
The City College of New York

Friday, March 1, 2013 - 10:00am - 11:00am

CoRE Building Lecture Hall

Title:    Selective "Light" Redirection For Information Processing and Energy Conservation


Processing “light” provides solutions in medical systems, metrology, telecommunications, industrial and environmental sensing, energy and sustainable environment. In this lecture, two examples of selective “light” redirection for signal processing and energy conservation will be presented. In the first example, the development of a tunable-bandwidth optical filter for applications in telecommunications, sensors and RF/microwave signal processing is described. The approach is based on an all-fiber-optic dynamically tunable Michelson-Gires-Tournois Interferometer (MGTI) that incorporates broadband fiber-optic based loop mirrors. These fiber loop mirrors provide a wide range of tunable relativity (e.g., 0-100%) and enable a variety of transfer functions, such as notch, rectangular pass-band and comb filters using the same basic platform. The second example highlights a more “classical” optical system that enables windows to become from the weakest link the in building envelope and energy “losers”, to energy managers and potentially energy “suppliers”. The concept is based on an active façade implementation for selective redirection of incoming solar radiation with a dual goal to harvest (a) the visible part of the spectrum for efficient daylighting and (b) the thermal energy of the incoming solar radiation for heating/cooling assistance. Both of these goal lead to reduced energy consumption, reduced carbon footprint and increased occupant comfort.


Dr. N. Madamopoulos is an Associate Professor in the Department of Electrical Engineering at the City College of New York (CCNY), of the City University of New York (CUNY). He is leading the research and educational efforts at the Optics and Photonics Systems Laboratory. He is also the Assistant Director of the Building Performance Lab, CUNY Institute for Urban Systems. Before joining CCNY, he held various Senior Research positions in the industry (Corning, Inc, Lucent – Bell Labs, Calient Networks) and research institutions (National Hellenic Research Foundation; Foundation For Research and Technology-Hellas, Department of Electrical and Computer Engineering -UCSB, The School of Optics/CREOL). His work has been widely recognized and honored with several international awards by the OSA, IEEE, and SPIE.

Professor Madamopoulos received a BS in Physics from the University of Patras, Greece, in 1993, a M.Sc. and a Ph.D. degree in Optical Science and Engineering from The College of Optics & Photonics/CREOL (Center for Research and Education in Optics and Lasers), University of Central Florida, in 1996 and 1998, respectively.

Dr. Irina Rish
IBM, Thomas J. Watson Research Center

Wednesday, February 27, 2013 - 10:00am - 11:00am

CoRE Building Lecture Hall

Sparsity and Interpretability in Predictive Multivariate Analysis of fMRI Data


One of the central topics in statistical analysis of fMRI data is discovering brain areas relevant to a given stimuli or mental state. Herein, we focus on predictive accuracy as a better relevance measure than the traditional voxel activations based on univariate voxel correlations with stimulus, since the latter approach ignores potentially important multivariate voxel interactions.

Since an exhaustive search over all subsets of voxels is intractable, sparse (l1-regularized) regression is a popular alternative for learning predictive models simultaneously with selection of predictive subsets of voxels, since l1 constraint on the regression model parameters tends to find solutions with relatively small number of nonzeros. However, multiple well-predicting sparse solutions may exist when the variables are highly correlated, as it is the case in fMRI data. Thus, while the brain areas included into a highly predictive sparse solution are clearly relevant to the task, it is not clear how unique such solutions are, i.e. how much information about the task is still contained in the rest of the brain.

This leads to the following questions: should one expect a sharp boundary between task-relevant and task-irrelevant brain areas, or rather a widespread distribution of task-relevant information across the whole brain? How does the task-related information distribution depend on the properties of the task? Herein, empirical exploration of such boundary is performed using the Elastic Net regression when predicting several stimuli and/or behavior variables from fMRI, including pain perception, visual stimulus rating, and several behavioral measures recorded during videogame playing (PBAIC 2007 data). Interestingly, for most of the tasks, no clear separation between relevant and irrelevant areas is observed, with the only exception of a relatively simple auditory task.

Thus, we hypothesize that complex tasks tend to involve most of the brain rather than just specific areas, which points in the direction of ``holographic'' information representation for such tasks, while simpler tasks yield more clear separation between relevant and irrelevant areas. These observations suggest a novel methodological approach that goes beyond traditional voxel activation maps and involves a more comprehensive evaluation of information spread in the brain.


Irina Rish , is a Research Staff Member (RSM) at IBM T.J. Watson Research Center. She received her MS in Applied Mathematics from Moscow Gubkin Institute, Russia, and PhD in Computer Science from the University of California, Irvine. Irina Rish research interests are in the areas of probabilistic inference, machine learning, and information theory. Particularly, she has done work on approximate inference in graphical models, information-theoretic experiment design and active learning, with applications are in the area of autonomic computing - automated management of complex distributed systems, which includes various diagnosis, prediction and online decision-making problems. Irina Rish’s current research is in the area of machine-learning applications to computational biology and neuroscience, with a particular focus on statistical analysis of brain imaging data such as fMRI.

Dr.. Naveen Verma
Princeton University

Wednesday, February 13, 2013 - 1:00pm - 2:00pm

CoRE Building Lecture Hall


Think about some of the physical systems with which we would like electronics to engage in valuable interactions: physiological systems, high‐value industrial equipment, critical infrastructure…. These systems are complex, both in terms of the number of signals they present, and in terms of how those signals represent information. In this talk I will describe some of the hardware platforms we are pursuing to handle these complexities. By ‘interacting with a physically‐complex world’, I am referring to an ability to make sense of embedded signals when in fact we may have no tractable analytical models for the underlying processes. Instead, we look at how sensor data can be used as a knowledge base, exploiting the tremendous data‐acquisition capabilities of next‐generation sensor networks towards the construction of high‐quality data‐driven models. Machine learning gives us powerful frameworks for data‐driven analysis; the question is how to create very‐low‐power hardware to enable such frameworks in energy‐constrained sensor devices. I will describe our work on low‐power medical sensors for disease monitoring and harm detection. Interacting with a physically‐complex world also implies the ability to acquire embedded signals on a very large scale, in fact much larger than traditional integrated‐circuit technologies can possibly handle. Large‐area electronics is a technology that can enable the creation of large arrays of diverse transducers for sensing. To build complete systems, however, substantial embedded computation, instrumentation, and power management is also required. We focus on scalable methods for combining large‐area electronics with CMOS ICs to exploit the complementary strengths of the two technologies. I will describe our work towards smart infrastructure, using flexible sensing sheets to build systems for high‐resolution structural‐health monitoring of bridges.


Dr. Verma received the B.A.Sc. degree in Electrical and Computer Engineering from the University of British Columbia, Vancouver, Canada in 2003 and the M.S. and Ph.D. degrees in Electrical Engineering from Massachusetts Institute of Technology in 2005 and 2009 respectively. Since July 2009 he has been an Assistant Professor of Electrical Engineering at Princeton University. His research focuses on advanced sensing systems, including low‐voltage digital logic and SRAMs, low‐noise analog instrumentation and data‐conversion, large‐area sensing arrays based on flexible electronics, and low‐ energy algorithms for embedded inference especially for medical applications.

Dr. Jason Stauth
Dartmouth College

Wednesday, February 6, 2013 - 10:00am - 12:00pm

CoRE Building Lecture Hall


The viability of photovoltaic energy has increased tremendously in the last decade as new manufacturing capacity (especially in China) has driven PV module costs to levels never thought possible. However, there remain challenges in achieving true grid-cost-parity for PV compared to other sources such as coal and natural gas. These challenges include balance of system components and system integration costs, both of which are now major focuses of the DOE. This talk will discuss PV systems from a general perspective, targeting audience members who have less exposure to the PV area. The basic configurations of PV systems will be presented from the cell, module, string, array, and power plant level. We will then drill down into the different architectures for the power management system including central inverter, micro-inverter, and other emerging architectures. New classes of multi- level DC-DC converter ‘partial-power processing’ circuits will be presented as next-generation solutions.


Dr. Stauth received his MS and PhD degrees at University of California, Berkeley in 2006 and 2008 respectively. At U.C. Berkeley Dr. Stauth was affiliated with the Berkeley Wireless Research Center (BWRC) and the Power Electronics Research Group, studying under Prof. Seth Sanders. His research focused on high frequency power electronics and RF integrated circuits. In 2008 he co-founded QVSense, Inc. and operated as CTO until QVSense was acquired by Solar Semiconductor. He remained as Director of Hardware Engineering at Solar Semiconductor until 2011. Since 2011 he has been Assistant Professor at Dartmouth College.

Dr. Trac D. Tran,
Johns Hopkins University

Wednesday, December 5, 2012 - 10:00am - 12:00pm

CoRE Building Lecture Hall


Most natural signals can be approximately represented by a few coefficients with respect to certain basis that carry the most relevant information. Processing of the signals in the sparsifying domain is much faster and simpler than in the original domain. This makes signal sparsity an extremely powerful tool in many classical signal processing applications. On the other hand, in many signal processing applications involving highly non-stationary signals such as images and videos, local adaptivity also plays a very important role in capturing the rich spatial/temporal/spectral correlation structure. In this talk, I will introduce the development of sparsity-constrained optimization algorithms based on content- or data-adaptive dictionary design for discriminative applications (detection, classification, and recognition). The proposed framework is applicable in a variety of practical discriminative applications, provided the assumption that signals lie in a union of low-dimensional subspaces holds.

I will particularly concentrate on target detection and classification for hyperspectral imagery. However, if time permits, I will also discuss other applications such as robust face recognition and other research directions in the area of sparse signal processing at Johns Hopkins.


Trac D. Tran S'94-M'98-SM'08 received the B.S. and M.S. degrees from the Massachusetts Institute of Technology, Cambridge, in 1993 and 1994, respectively, and the Ph.D. degree from the University of Wisconsin, Madison, in 1998, all in Electrical Engineering.

In July of 1998, Dr. Tran joined the Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, where he was recently promoted to the rank of Professor. His research interests are in the field of digital signal processing, particularly in sparse representation, sparse recovery, sampling, multi-rate systems, filter banks, transforms, wavelets, and their applications in signal analysis, compression, processing, and communications. His pioneering research on integer-coefficient transforms and pre-/post-filtering operators has been adopted as critical components of Microsoft Windows Media Video 9 and JPEG XR - the latest international still-image compression standard ISO/IEC 29199-2.

Dr. Tran was the co-director (with Prof. J. L. Prince) of the 33rd Annual Conference on Information Sciences and Systems (CISS'99), Baltimore, MD, in March 1999. In the summer of 2002, he was an ASEE/ONR Summer Faculty Research Fellow at the Naval Air Warfare Center & Weapons Division (NAWCWD) at China Lake, California. He is currently a regular consultant for the U.S. Army Research Laboratory in Adelphi, Maryland. Dr. Tran has served as Associate Editor of the IEEE Transactions on Signal Processing as well as IEEE Transactions on Image Processing. He was a former member of the IEEE Technical Committee on Signal Processing Theory and Methods (SPTM TC) and is a current member of the IEEE Image Video and Multidimensional Signal Processing (IVMSP) Technical Committee. He is currently serving his second term as an Associate Editor for IEEE Transactions on Signal Processing.

Prof. Tran received the NSF CAREER award in 2001, the William H. Huggins Excellence in Teaching Award from The Johns Hopkins University in 2007, and the Capers and Marion McDonald Award for Excellence in Mentoring and Advising in 2009.

Dr. Vijay Narayanan, Pennsylvania State University

Wednesday, November 14, 2012 - 10:00am - 12:00pm

CoRE Building Lecture Hall


While machine vision research has continued to improve multi-fold over the past few decades, it still significantly falls short of the abilities and efficiencies of the primate visual cortex system. The primate brain is especially superior as pertains to comprehending and interacting with complex natural environments. In energy-efficiencies, the brain is estimated to be around four orders better than current machine vision solutions. While there is much consensus on the superiority of biological vision systems over machine systems on most vision tasks, the approaches leading to the better efficiencies and flexibility akin to the visual cortex are still widely debated. In this talk, I will highlight recent efforts at architecting customized digital hardware systems using neuromorphic algorithms as one successful approach to achieving better energy efficiencies. I will also provide insight to how emerging devices are influencing novel implementations of such hardware systems.


Vijaykrishnan Narayanan is a Professor of Computer Science and Engineering and Electrical Engineering at The Pennsylvania State University. His research and teaching interests include embedded systems, computer architecture, system design using emerging device technologies and power-aware computing. He has deep interests in cross-disciplinary advances and has led and participated in such projects. He is the deputy editor-in-chief of IEEE TCAD and served as the editor-in-chief for ACM Journal of Emerging Technologies in Computing Systems. He has won several awards including the 2012 ASPDAC Ten-year retrospective Most influential paper, 2012 Penn State Alumni Society Premier Research Award and 2010 Outstanding Alumnus Award from SVCE, India. He is a fellow of IEEE. He has worked with several outstanding students who are currently in industry and academia throughout the world.

Dr. Minjoo Lee, Yale University

Wednesday, November 7, 2012 - 10:00am - 12:00pm

CoRE Building Lecture Hall


In this talk, I will present progress on three projects in my group that are unified by their need for strained or lattice-mismatched growth. The first topic is molecular beam epitaxy (MBE) of metamorphic InGaP and GaAsP for solar cells. Our interest in metamorphic InGaP stems from the fact that its bandgap can be tuned to values as high as ~2.2 eV. Such high bandgap materials will be necessary for multi-junction solar cells to reach 60% efficiency, but mismatched epitaxy is required since the compositions of interest do not possess the same lattice constant as common substrate materials. In contrast, metamorphic GaAsP is needed as a top junction on Si for high-efficiency, low- cost applications. I will discuss how control over extended defects in these materials led to our demonstration of high open-circuit voltage InGaP and GaAsP cells on both GaAs and GaP substrates.

Next, I will discuss the strain-driven growth and luminescence properties of InGaAs quantum dots (QDs) on GaP. Since GaP is nearly lattice-matched to Si, these dots represent a new approach to efficient light emission on Si. Results on incorporation of InGaAs/GaP QDs into light-emitting diodes and photonic crystal cavities will be presented. The third topic is my group’s recent discovery that dislocation-free, tensile-strained nanostructures can be grown on (110) and (111) substrates. This result is unusual, since self-assembled island growth is typically driven by compressive strain on the (001) surface. I will present our analysis of these findings in terms of dislocation kinetics, as well as new results on tensile-strained GaAs QDs grown on In0.52Al0.48As/InP(110).


Dr. Minjoo Larry Lee received the Sc.B. with honors in materials science and engineering from Brown University, Providence, RI in 1998 and the Ph.D. in electronic materials from the Massachusetts Institute of Technology (MIT), Cambridge, MA in 2003. From 2003 to 2006, he was a Postdoctoral Researcher with the Microsystems Technology Laboratory at MIT. From 2006-2007, he was with the Center for Thermoelectrics Research at RTI International in Durham, NC, and in 2008 he joined Yale University in New Haven, CT as an assistant professor of electrical engineering. He is the author or coauthor of over 80 technical papers and refereed conference proceedings and holds seven patents. He and his students have received numerous recognitions including: the DARPA Young Faculty Award, NSF CAREER award, the MRS gold award for graduate student research, the IEEE EDS George E. Smith award, and the North American MBE conference best student paper prize. In 2012, he was named Lange Lecturer in Materials at the University of California Santa Barbara in recognition of “outstanding contributions to strain engineered semiconductor materials”.

Postponed -- This colloquium will be rescheduled for a later date.

Dr. Jason T. Stauth, Dartmouth College

Wednesday, October 31, 2012 - 10:00am - 12:00pm

CoRE Building Lecture Hall

Photovoltaic Energy: A Circuits and Systems Perspective


The viability of photovoltaic energy has increased tremendously in the last decade as new manufacturing capacity (especially in China) has driven PV module costs to levels never thought possible. However, there remain challenges in achieving true grid-cost-parity for PV compared to other sources such as coal and natural gas. These challenges include balance of system components and system integration costs, both of which are now major focuses of the DOE. This talk will discuss PV systems from a general perspective, targeting audience members who have less exposure to the PV area. The basic configurations of PV systems will be presented from the cell, module, string, array, and power plant level. We will then drill down into the different architectures for the power management system including central inverter, micro-inverter, and other emerging architectures. New classes of multi-level DC-DC converter ‘partial-power processing’ circuits will be presented as next-generation solutions.


Dr. Stauth received his MS and PhD degrees at University of California, Berkeley in 2006 and 2008 respectively. At U.C. Berkeley Dr. Stauth was affiliated with the Berkeley Wireless Research Center (BWRC) and the Power Electronics Research Group, studying under Prof. Seth Sanders. His research focused on high frequency power electronics and RF integrated circuits. In 2008 he co-founded QVSense, Inc. and operated as CTO until QVSense was acquired by Solar Semiconductor. He remained as Director of Hardware Engineering at Solar Semiconductor until 2011. Since 2011 he has been Assistant Professor at Dartmouth College.

Dr. Donglei Fan, University of Texas

Friday, October 26, 2012 - 10:00am - 12:00pm

CoRE Builidng 7th Floor Boardroom


Electric tweezers utilize DC and AC electric fields through voltages applied on patterned electrodes to manipulate nanoentities suspended in a liquid. Nanowires with a large aspect ratio are particularly suitable for use in electric tweezers for patterning, assembling, and manipulation. Despite operating in the regime of extremely small particle Reynolds number (of order 10−5), electric tweezers can manipulate nanowires with high precision to follow any prescribed trajectory, to rotate nanowires with controlled chirality, angular velocity and rotation angle. Electric tweezers have been applied to assemble nanowires into arrays of optical nanosensors, to determine the electronic properties of nanomaterials from their mechanical rotation in a noncontact and non-destructive manner, and to construct nanoelectromechanical system (NEMS) devices such as nanomotors and nano-oscillators. Electric tweezers have also been used to transport drug-carrying functionalized nanowires for cell-specific drug delivery.


Dr. Donglei (Emma) Fan is an Assistant Professor in Department of Mechanical Engineering of the University of Texas at Austin since 2010. She received National Science Foundation CAREER award in 2012. Dr. Fan obtained her bachelor’s degree in chemistry from the Department of Intensive Instruction, an honor program for gifted youth, in Nanjing University, China, in 1999, and doctoral (2007) degrees in Materials Science and Engineering from the Johns Hopkins University (JHU). Prof. Fan’s work has spurred a series of publications on journals including Nature Nanotechnology, the Proceedings of National Academy of Science, Nano Today, Physical Review Letters, Applied Physics Letters, Advanced Materials, as well as three patent disclosures. Her work was widely reported by the academic news media such as Nature Nanotechnology, MRS Bulletin,, APS news, and had been selected multiple times by Virtual Journals of Nanoscale Science and Technology.

Dr. Sundeep Rangan, New York University - Polytechnic Institute

Wednesday, October 24, 2012 - 10:00am - 12:00pm

CoRE Building Room 701 Boardroom


Sparsity has emerged as a powerful modeling concept in a wide range of signal processing and learning problems: Many high-dimensional objects admit a sparse representation in a suitable transform basis and the recent field of compressed sensing has provided efficient methods to exploit this sparse structure in estimation, dimensionality reduction and feature extraction. However, sparsity is generally not the only aspect of many practical inverse problems. This talk presents a powerful new class of algorithms called generalized approximate message passing (GAMP) that significantly extends the compressed sensing framework. The GAMP algorithm use Gaussian approximations of loopy belief propagation to reduce estimation problems on complex interconnected systems to smaller, local problems associated with the individual system components. For compressed sensing estimation, the GAMP method can incorporate arbitrary priors and nonlinearities, perform joint estimation of latent variables, and can exploit large classes of complex statistical relationships between variables. Moreover, for certain inverse problems with large random transforms, the GAMP method admits remarkably precise asymptotic performance characterizations with testable conditions for optimality -- even for problems that are non-convex and nonlinear. Applications are demonstrated in challenging identification problems in neuroscience and in optimization problems in wireless interference coordination. Joint work with Alyson Fletcher (UCSC), Phil Schniter (Ohio State), Ulugbek Kamilov (EPFL), Vivek K Goyal (MIT) and Lav Varshney (IBM).


Dr. Sundeep Rangan received the B.A.Sc. at the University of Waterloo, Canada and the M.Sc. and Ph.D. at the University of California, Berkeley, all in Electrical Engineering. He has held postdoctoral appointments at the University of Michigan, Ann Arbor and Bell Labs. In 2000, he co-founded (with four others) Flarion Technologies, a spin off of Bell Labs, that developed Flash OFDM, one of the first cellular OFDM data systems, and precursor to 4G cellular standards such as LTE and WiMax. Flarion grew to over 150 employees with trials worldwide. In 2006, Flarion was acquired by Qualcomm Technologies where Dr. Rangan was a Director of Engineering involved in OFDM infrastructure products. He joined the ECE department at NYU-Poly in 2010. His research interests are in wireless communications, signal processing, information theory and control theory.

Dr. Changho Suh, Korea Advanced Institute of Science and Technology

Wednesday, October 10, 2012 - 10:00am - 12:00pm

CoRE Building Lecture Hall


The computation problem in networks represents a task-oriented communication setup where receivers wish to compute functions of multiple sources. While the problem has received considerable attention with applications in sensor networks and cloud computing scenarios, only the simplest setting has been well understood thus far which consists of a single receiver wanting a linear function of the sources: It is shown that the cut-set bound is tight in the single-receiver case. Beyond this case, we are still lacking in our understanding. Even for the next simplest setting which includes another receiver with the same demand, it is not known whether the cut-set bound is tight.

In this talk, I will present our recent progress on the problem of multicasting a linear function to multiple receivers. What we have shown is twofold. First we find that a function-matching network structure enables higher computation rates than the separation scheme can provide. Secondly we show that unlike the single-receiver case, the cut-set bound is not tight when multicasting a function. In the process of deriving this conclusion, we characterize the computing capacity of a two-transmitter two-receiver linear deterministic network where both receivers want to decode a mod-2 sum of two independent Bernoulli sources generated at the two transmitters.

Furthermore, we extend our results to a more general setting, thus characterizing the computing capacity of a class of L transmitter L receiver networks in the limit of L. We believe our achievability and converse theorem can be used as key tools to address general computation problems in networks.


Dr. Changho Suh is an Assistant Professor in the Department of Electrical Engineering at Korea Advanced Institute of Science and Technology (KAIST) since 2012. He received the B.S. and M.S. degrees in Electrical Engineering from KAIST in 2000 and 2002 respectively, and the Ph.D. degree in Electrical Engineering and Computer Sciences from UC-Berkeley in 2011, under the supervision of Prof. David Tse. From 2011 to 2012, he was a postdoctoral associate at the Research Laboratory of Electronics in MIT. From 2002 to 2006, he had been with the Telecommunication R&D Center, Samsung Electronics. Prof. Suh received the David J. Sakrison Memorial Prize for outstanding doctoral research from the UC-Berkeley EECS Department in 2011, the Best Student Paper Award of the IEEE International Symposium on Information Theory in 2009 and the Outstanding Graduate Student Instructor Award in 2010. He was awarded several fellowships, including the Vodafone U.S. Foundation Fellowship in 2006 and 2007; the Kwanjeong Educational Foundation Fellowship in 2009; and the Korea Government Fellowship from 1996 to 2002.

Dr. Nicolas H. Younan, Professor and Chair, Mississippi State University

Wednesday, October 3, 2012 - 10:00am - 12:00pm

CoRE Building Lecture Hall

Scientific Challenges in Information Retrieval from Earth Observation (EO) Imagery


The ultimate goal of any Earth Observation (EO) system is to provide understanding. However, this understanding will often require expertise and/or data sources from globally distributed resources and will present some unique challenges for the local remote sensing analyst. To address these challenges, it is incumbent upon the global community to evolve and sustain a global observation network. These observations serve as the foundation for the models that are used to describe Earth processes. As this observational data accumulates in global archives new opportunities become available for knowledge discovery about the Earth system. However, access to these observational data is optimized for the science teams for whom the instruments were launched and access by operational users may be problematic. This presentation will lay out some of the Grand Challenges for those engineers and scientists involved in pattern recognition in the Earth remote sensing arena. It describes the problem space for making local decisions and introduces the concept of contextual remote sensing. Answers will require addressing the multi-dimensional aspects of the EO imagery – hyper-spatial, hyper-spectral, hyper-temporal, and hyper sensors.


Nicolas H. Younan is currently the Department Head and James Worth Bagley Chair of Electrical and Computer Engineering at Mississippi State University. He received the B.S. and M.S. degrees from Mississippi State University, in 1982 and 1984, respectively, and the Ph.D. degree from Ohio University in 1988. Dr. Younan’s research interests include signal processing and pattern recognition. He has been involved in the development of advanced image processing and pattern recognition algorithms for remote sensing applications, image/data fusion, feature extraction and classification, automatic target recognition/identification, and image
information mining.

Dr. Younan has published over 200 papers in refereed journals and conference proceedings. He has served as the General Chair and Editor for the 4th IASTED International Conference on Signal and Image Processing, Co-Editor for the 3rd International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, Guest Editor, Pattern Recognition Letters and IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), and Co-Chair, 4th and 5th Workshop on Pattern Recognition for Remote sensing (PRSS). He is a senior member of IEEE and a member of the IEEE Geoscience and Remote Sensing society, serving on two technical committees: Data Fusion, and Data Archive and Distribution. He also served as the Vice Chair of the International Association on Pattern Recognition (IAPR) Technical Committee 7 on Remote Sensing (2008-2010).

Dr. Kamin Whitehouse, University of Virginia

Wednesday, September 26, 2012 - 10:00am - 12:00pm

CoRE Building Lecture Hall

The Rise of the Practical Smart Home


People spend 60% of their time in their homes, and the performance of a home affects every aspect of a person's life. As such, "smart homes" have real potential to help people better understand and manage aspects of their own lives, including their energy consumption, medical health, and family function. This talk will present a new smart home system that can detect who is in the home, which rooms they are in, and what they are doing in terms of water fixture and electrical fixture usage -- but without instrumenting the people or fixtures, and without using cameras, microphones, or the use of wearable tags or transmitters. A main goal of the system is to be affordable and easy to install: in production version, it is expected to cost less than a new television, and to be installed in minutes.


Dr. Kamin Whitehouse works in the areas of wireless sensor networks, with a focus on applications in energy conservation. He is an associate professor in the Computer Science Department at the University of Virginia, is a Siebel scholar, and was awarded NSF CAREER award. He earned his BA and BS from Rutgers University and his MS and Ph.D. from UC Berkeley.

Dr. Kyung Ryu, IBM T.J. Watson Research Center

Thursday, September 20, 2012 - 10:00am - 12:00pm

new location: CoRE Building Room 701 Boardroom


It has been more than a decade since the Blue Gene project started in IBM Research. Blue Gene/Q, the latest and third generation of Blue Gene, is again at the top of the Supercomputer TOP500 and Green500 lists.

This talk will highlight the evolution of the Blue Gene technologies and its revolutionary impact on Sciences. I will also discuss the new challenges and directions of Supercomputing beyond Blue Gene.


Dr. Kyung Dong Ryu is Manager and Research Project Leader of the Exascale System Software Research Group at the IBM T.J. Watson Research Center. Since 2004, he has led several research projects in High Performance Computing, Cloud Computing, Resilient HA Cluster, End-to-End System Performance Optimization. He had also led the scalable system management project for datacenters, advancing IBM’s systems management products, for which he received an Outstanding Technical Achievement Award from IBM. Before joining IBM Research, he was an Assistant Professor at the Arizona State University (2001-2004), where his research focused on PC Grid and Operating Systems for High Performance Computing. As a faculty member, he was awarded NSF grants and led several industrial research projects funded by the Research Consortium with Intel and Motorola. He published over 40 papers in premier international conferences and journals, including SC, ICS, IPDPS, ICDCS, TPDS, Eurosys, and ISCA. He received M.S. and Ph.D. in 1998 and 2001, respectively, from the University of Maryland, College Park, in Computer Science.

Prof. Janne Lindqvist, Rutgers University, WINLAB

Wednesday, September 12, 2012 - 10:00am - 12:00pm

CoRE Building Lecture Hall


Many location-sharing systems have been developed over the past 20 years, but only recently have these systems started to be adopted by consumers. In this talk, we will discuss our work on understanding users and their use of location-sharing applications, and how people manage their privacy with real applications.

We will also introduce some of our ongoing research using location information to combat distracted driving and talk about crowdsourcing for understanding privacy and creating social connections between people.



Janne Lindqvist is an Assistant Research Professor at WINLAB/ECE, Rutgers University. Prior to Rutgers, Janne was a post-doc with the Human-Computer Interaction Institute at Carnegie Mellon University. Janne received his M.Sc. degree in 2005 and D.Sc. degree in 2009, both in Computer Science and Engineering from Helsinki University of Technology, Finland.

He works at the intersection of mobile computing, systems security and human-computer interaction. Before joining the academia, Janne co-founded a wireless networks company, Radionet, which was represented in 24 countries before being sold to Florida-based Airspan Networks in 2005.

During his first year at Rutgers, Janne was awarded three NSF awards totaling ca. $1.3 million.

Professor Ness B. Shroff, Ohio Eminent Scholar in Networking and Communications
Chaired Professor of ECE and CSE

Wednesday, April 25, 2012 - 10:00am - 12:00pm

CoRE Building Lecture Hall


The dramatic increases in demands from multimedia applications are putting an enormous strain on current cellular system infrastructure. Hence, we are witnessing significant research and development efforts on 4G multi-channel wireless cellular systems (e.g., LTE and WiMax) that target new ways to achieve higher data rates, lower latencies, and a much better user experience. An important requirement for achieving these goals is to design efficient scheduling policies that can simultaneously provide high throughput and low delay. In these multi-channel systems, such as OFDM, the Transmission Time Interval (TTI), within which the scheduling decisions need to be made, is typically on the order of a few milliseconds. On the other hand, there are hundreds of orthogonal channels that can be allocated to different users. Hence, many decisions have to be made within a short scheduling cycle and it is critical that scheduling policies must be of low complexity. In this talk, we will present a unifying framework for designing low-complexity scheduling policies in the downlink of multi-channel (e.g., OFDM-based) wireless networks that can provide optimal performance in terms of both throughput and delay. We first develop new easy-to verify sufficient conditions for rate-function delay-optimality in the many-channel many-user asymptotic regime. and for throughput-optimality in non-asymptotic settings.

The sufficient conditions enable us to prove rate-function delay optimality for a class of Oldest Packets First (OPF) policies and throughput optimality for a large class of Maximum Weight in the Fluid limit (MWF) policies, respectively. While a recently developed scheduling policy is both throughput-optimal and rate-function delay-optimal, it has a very high complexity of O(n^5), where n is the number of channels or users, which makes it impractical. By intelligently combining policies from the classes of OPF policies and MWF policies, we design hybrid policies that have a low complexity of O(n^{2.5} log n), without losing either throughput-optimality or rate-function delay-optimality. We further develop two simpler greedy policies that are both throughput-optimal and have a positive rate-function. We show through simulations that empirically these simpler mechanisms have near-optimal value of rate-function in various scenarios. Finally, we propose a class of throughput-optimal policies with even lower complexity that allows an explicit trade-off between complexity and delay performance.


Ness Shroff received his Ph.D. degree in Electrical Engineering from Columbia University in 1994. He joined Purdue university immediately thereafter as an Assistant Professor in the school of ECE. At Purdue, he became Full Professor of ECE in 2003 and director of CWSA in 2004, a university-wide center on wireless systems and applications. In July 2007, he joined The Ohio State University, where he holds the Ohio Eminent Scholar endowed chair professorship in Networking and Communications, in the departments of ECE and CSE. Since 2009, he also serves as a Guest Chaired professor of Wireless Communications at Tsinghua University, Beijing, China. His research interests span the areas of communication, social, and cyberphysical networks. He is especially interested in fundamental problems in the design, control, performance, pricing, and security of these networks. Dr. Shroff is a past editor for IEEE/ACM Trans. on Networking and the IEEE Communication Letters. He currently serves on the editorial board of the Computer Networks Journal, IEEE Network Magazine, and the Networking Science journal. He has chaired various conferences and workshops, and co-organized workshops for the NSF to chart the future of communication networks. Dr. Shroff is a Fellow of the IEEE and an NSF CAREER awardee. He has received numerous best paper awards for his research, e.g., at IEEE INFOCOM 2008, IEEE INFOCOM 2006, IEEE IWQoS 2006, Journal of Communication and Networking 2005, Computer Networks 2003, and one of two runner-up papers at IEEE INFOCOM 2005.

Professor Dario Pompili
Dept ECE, Rutgers University

Wednesday, April 18, 2012 - 10:00am - 12:00pm

CoRE Building Lecture Hall


Mobile platforms are becoming the predominant medium of access to Internet services due to the tremendous increase in their computation and communication capabilities. However, enabling applications that require real-time in-the-field data collection and processing using mobile platforms is still challenging due to i) the insufficient computing capabilities and unavailability of complete data on individual devices and ii) the prohibitive communication cost and response time involved in offloading data to remote centralized computing resources such as cloud datacenters.

This talks presents a novel resource provisioning framework for organizing the heterogeneous sensing, computing, and communication capabilities of static and mobile devices in the vicinity in order to form an elastic resource pool – a hybrid static/mobile computing grid. This local computing grid can be harnessed to enable innovative data- and compute-intensive mobile applications such as ubiquitous context-aware health and wellness monitoring of the elderly, distributed rainfall and flood-risk estimation, distributed object recognition and tracking, and content-based distributed multimedia search and sharing.

In order to address challenges such as the inherent uncertainty in the hybrid grid (in terms of network connectivity and device availability), a role-based resource-provisioning framework imparted with autonomic capabilities (self-organization, self-optimization, and self-healing) is proposed.


Dario Pompili joined as Assistant Professor the faculty of the Department of Electrical and Computer Engineering (ECE) at Rutgers University in 2007, where he is the Director of the Cyber Physical Systems Laboratory (CPS Lab). He is also co-directing the Cloud and Autonomic Computing (CAC) Center, an NSF multi-institution research center funded by the I/UCRC program.

He received a Ph.D. in ECE from the Georgia Institute of Technology in 2007 after working at the Broadband Wireless Networking Laboratory (BWN-Lab). In 2005, he was awarded Georgia Institute of Technology BWN-Lab Researcher of the Year for “outstanding contributions and professional achievements". He had previously received his “Laurea” (integrated B.S. and M.S.) and Doctorate degrees in Telecommunications Engineering and System Engineering from the University of Rome “La Sapienza,” Italy, in 2001 and 2004.

His research interests include wireless sensor networks, underwater acoustic communication and coordination of underwater vehicles, green computing, and network optimization and control. He is author of many influential research articles on these topics. He serves on the editorial board of Ad Hoc Networks (Elsevier) Journal and on the technical program committee of several leading conferences on networking such as INFOCOM, MASS, SECON, GLOBECOM, ICC. In 2011, he received the NSF CAREER award for his work on underwater multimedia acoustic communication and the Rutgers/ECE Outstanding Young Researcher award. He has recently won a Young Investigator Program (YIP) grant from the ONR, one of only 26 awarded nationwide in 2012, for his proposal titled “Investigating Fundamental Problems for Real-time In-situ Data Processing in Heterogeneous Mobile Computing Grids.” He is member of the IEEE Communications Society and the ACM.

Professor Shantenu Jha, Department of Electrical & Computer Engineering,
Rutgers University

Wednesday, April 11, 2012 - 10:00am - 12:00pm

CoRE Building Lecture Hall


Many scientifically important questions require the efficient use of high-performance and distributed computing in order to provide answers with the accuracy needed at the length and time-scales required. We begin by analyzing how and why it has been necessary to develop "effective abstractions" in order to successfully utilize production high-performance distributed cyber-infrastructure, such as NSF's TeraGrid/XSEDE. For example, pilot-jobs are arguably one of the most widely-used distributed computing abstractions, and have been shown to support scalable and dynamic utilization of distributed resource.

However, there does not exist a well-defined, unifying conceptual model of pilot-jobs which can be used to define, compare and reason across different implementations of pilot-jobs; this presents a barrier to extensibility and interoperability. We introduce the P* Model the first known conceptual model of pilot-jobs, validate its implementation via the Pilot-API -- by concurrently using multiple distinct pilot-job frameworks on distinct production distributed cyber-infrastructures, and propose extensions of the P* Model to data.

We will discuss the application of the pilot-abstraction to support the infrastructural and algorithmic requirements of several Grand Challenge problems facing the Computational Biology community, for example data-analytics for next-generation gene sequencing, enhanced sampling molecular algorithms, and in-silico personalized and predictive health-care.

For Graduate students looking for research opportunities, we will conclude by providing a brief overview of the Radical group and some research topics currently of interest.


Prof. Shantenu Jha is an Assistant Professor at Rutgers University, a member of the Graduate Faculty in the School of Informatics at the University of Edinburgh (UK), and a Visiting Scientist at University College London. Before moving to Rutgers, he was the lead for Cyberinfrastructure Research and Development at the CCT at Louisiana State University. His research interests lie at the triple point of Computer Science, Cyberinfrastructure Development and Computational Science.

Shantenu is the lead investigator of the SAGA project (, which is a community standard and is part of the official middleware/software stack of most major Production Distributed Cyberinfrastructure -- such as US NSF's XSEDE and the European Grid Infrastructure. His research has been funded by multiple NSF awards, US National Institute for Health (NIH) as well as the UK EPSRC (OMII-UK project and Research theme at the e-Science Institute). Jha is the author of approximately 60 publications; he has won some of the most prestigious awards in high-performance computing at ACM/IEEE Supercomputing and the International Supercomputing Series.

Professor Jha is writing a book on "Abstractions for Distributed Applications and Systems: A Computational Science Perspective". Jha seeks fearless and revolutionary young minds to join the RADICAL (thinking) group! Away from work, Jha tries middle-distance running and biking, tends to be an economics-junky, enjoys reading and writing random musings and tries to use his copious amounts of free time with a conscience.

Professor Waheed U. Bajwa, Department of Electrical & Computer Engineering,
Rutgers University

Wednesday, April 4, 2012 - 10:00am - 12:00pm

CoRE Building Lecture Hall


The problem of model selection arises in a number of statistics and signal processing applications, such as subset selection in linear regression, estimation of structures in graphical models, and signal denoising. In this talk, we introduce a simple algorithm, termed one-step thresholding (OST) algorithm, for model-order agnostic model selection in linear inference problems. We utilize two geometric measures of coherence, namely, worst-case coherence and average coherence, among the columns of a design matrix to provide an in-depth analysis of OST for model selection. One of the key insights offered by the ensuing analysis is that OST can successfully carry out model selection even when methods based on convex optimization such as the lasso fail due to the rank deficiency of the submatrices of the design matrix. In addition, we show that OST has the ability to perform near-optimally for a number of generic (random or deterministic) matrices as long as the design matrix satisfies conditions that are easily computable in polynomial time -- an area of great interest in applications such as genetic biomarker identification using gene expression data.


Waheed U. Bajwa received BE (with Honors) degree in electrical engineering from the National University of Sciences and Technology, Pakistan in 2001, and MS and PhD degrees in electrical engineering from the University of Wisconsin-Madison in 2005 and 2009, respectively. He was a Postdoctoral Research Associate in the Program in Applied and Computational Mathematics at Princeton University from 2009 to 2010, and a Research Scientist in the Department of Electrical and Computer Engineering at Duke University from 2010 to 2011. He is currently an Assistant Professor in the Department of Electrical and Computer Engineering at Rutgers University. His research interests include high-dimensional inference and inverse problems, compressed sensing, statistical signal processing, wireless communications, and applications in biological sciences, complex networked systems, and radar & image processing.

Dr. Bajwa was affiliated with Communications Enabling Technologies, Pakistan – the research arm of Avaz Networks Inc., Irvine, CA (now Quartics LLC) – from 2000-2003, with the Center for Advanced Research in Engineering, Pakistan during 2003, and with the RF and Photonics Lab of GE Global Research, Niskayuna, NY during the summer of 2006. He received the Best in Academics Gold Medal and President's Gold Medal in Electrical Engineering from the National University of Sciences and Technology (NUST) in 2001, and the Morgridge Distinguished Graduate Fellowship from the University of Wisconsin-Madison in 2003. He was Junior NUST Student of the Year (2000), Wisconsin Union Poker Series Champion (Spring 2008), and President of the University of Wisconsin-Madison chapter of Golden Key International Honor Society (2009). He served as a Guest Associate Editor for a special issue of Elsevier Physical Communication Journal on “Compressive Sensing in Communications” (2010-2011). He is currently a member of the IEEE, Pakistan Engineering Council, and Golden Key International Honor Society.

Dr. Stephen Weston
JP Morgan

Monday, April 2, 2012 - 10:00am - 12:00pm

CoRE Building Lecture Hall


Pressure on financial models and time to market create a relationship between speed of computation and business success. This talk explains how the application of dataflow techniques by JP Morgan has achieved cutting edge performance, whilst also helping to improve trading strategy, decision making and risk management.


Stephen is currently head of the Applied Analytics group within the investment banking division of JP Morgan. The group is responsible for accelerating mathematical models across asset classes for trading and risk management using dataflow computational techniques. Prior to joining JP Morgan Stephen spent lengthy periods at Deutsche Bank, Credit Suisse, Barclays and UBS. Prior to entering investment banking he also spent 5 years as a university lecturer teaching mathematical economics, banking, finance and monetary theory. Stephen holds a PhD in mathematical finance from Cass Business School in London.

Professor Jie Wu,
Department of Computer & Information Sciences,
Temple University

Wednesday, March 21, 2012 - 10:00am - 12:00pm

CoRE Building Lecture Hall


A paramount concern in dynamic wireless networks is efficient utilization of limited resources. The dynamic nature of wireless networks makes it difficult to use limited resources in a cost-efficient way. The traditional single-utility model, such as link quality, is inadequate for addressing this problem. In this talk, we discuss a composite utility model and use the routing problem in dynamic wireless networks as its application. Specifically, we integrate cost and link quality into a single network utility metric together with the benefiit of successful delivery of a routing packet to evaluate routing optimality. An efficient algorithm design, both centralized and distributed, is presented. Finally, several extensions to the basic model are discussed.


Jie Wu is chair and professor in the Department of Computer and Information Sciences at Temple University. Prior to joining Temple University, he was a program director at the National Science Foundation. His research interests include: wireless networks, mobile computing, routing protocols, fault-tolerant computing, and interconnection networks. He has published more than 550 papers in various journals and conference proceedings. He serves in the editorial boards of IEEE Transactions on Computers and Journal of Parallel and Distributed Computing. Dr. Wu was also general co-chair for IEEE MASS 2006, IEEE IPDPS 2008, and DCOSS 2009 and is the program co-chair for IEEE INFOCOM 2011. He has served as an IEEE Computer Society distinguished visitor. Currently, he is the chair of the IEEE Technical Committee on Distributed Processing (TCDP), an ACM distinguished speaker and a Fellow of the IEEE. Dr. Wu is the recipient of 2011 China Computer Federation (CCF) Overseas Outstanding Achievement Award.

Professor Jeffrey Walling

Department of Electrical & Computer Engineering,
Rutgers University

Wednesday, March 7, 2012 - 10:00am - 12:00pm

CoRE Building Lecture Hall


Wireless devices and sensors are increasingly ubiquitous in all aspects of life. As a result, researchers have worked tirelessly to provide more functionality and ever higher data rates to the devices. Researchers are challenged to use energy more efficiently, due to finite battery capacity and increasingly as everyone is asked to reduce their demands from the electric grid. In this talk I will address the challenge of using energy more efficiently in wireless communications systems by leveraging linearization around CMOS switching amplifiers. These switching amplifier topologies provide means to increase output power, efficiency and integratability of the PA with the rest of the radio circuitry, a major stumbling block in the quest for the RF system-on-a-chip (SOC).

I will summarize why switching amplifiers can outperform their linear counterparts and offer potential for tunability and reconfigurability for software defined radio (SDR) applications. Next I will motivate linearization methods that allow switching PAs to be used with non-constant envelope modulation, including pulse-width and -position modulation (PWPM) and envelope elimination and restoration (EER). The switched-capacitor PA, a topology that utilizes switched capacitors to enable a significant improvement in average efficiency and linearity utilizing a combination of data converter and power amplifier techniques will be introduced. It represents an exciting path towards SDR ready CMOS power amplifiers. I will conclude the talk with a few interesting research directions in energy efficient RF CMOS circuit design.


Dr. Walling received the B.S. degree from the University of South Florida, Tampa, in 2000, and the M.S. and Ph. D. degrees from the University of Washington, Seattle, in 2005 and 2008, respectively. Prior to starting his graduate education he was employed at Motorola, Plantation, FL working in cellular handset development. He interned for Intel, Hillsboro from 2006-2007, where he worked on highly-digital transmitter architectures and CMOS power amplifiers and continued this research while a Postdoctoral Research Associate with the University of Washington. He is currently an assistant professor in the electrical and computer engineering department at Rutgers, The State University of New Jersey.

His current research interests include low-power wireless circuits, energy scavenging, high-efficiency transmitter architectures and CMOS power amplifier design for software defined radio. Dr. Walling has authored over 30 articles in peer reviewed journals and refereed conferences. He received the Yang Award for outstanding graduate research from the University of Washington, Department of Electrical Engineering in 2008, an Intel Predoctoral Fellowship in 2007-2008, and the Analog Devices Outstanding Student Designer Award in 2006.

Professor Steven Weber, Electrical & Computer Engineering
Drexel University

Wednesday, February 29, 2012 - 10:00am - 12:00pm

CoRE Building Lecture Hall

Abstract: Transmission capacity (TC) is a performance metric for wireless networks that measures the spatial intensity of successful transmissions per unit area, subject to a constraint on the permissible outage probability (where outage occurs when the SINR at a receiver is below a threshold). I present a unified treatment of the TC framework that has been developed over the past decade. The mathematical framework underlying the analysis is stochastic geometry: Poisson point processes model the locations of interferers, and (stable) shot noise processes represent the aggregate interference seen at a receiver. I first present TC results (exact, asymptotic, and bounds) on a simple model in order to illustrate a key strength of the framework: analytical tractability yields explicit performance dependence upon key model parameters. I then present enhancements to this basic model --- channel fading, variable link distances, and multi-hop. Time permitting, I will discuss four network design case studies well-suited to TC: i) spectrum management, ii) interference cancellation, iii) signal threshold transmission scheduling, and iv) power control.


Steven Weber received his B.S. degree in 1996 from Marquette University in Milwaukee, WI, and his M.S. and Ph.D. degrees from The University of Texas at Austin in 1999 and 2003 respectively. He joined the Department ofElectrical and Computer Engineering at Drexel University in 2003 where he is currently an associate professor. His research interests are centered around mathematical modeling of computer and communication networks, specifically streaming multimedia and ad hoc networks.

Professor Jaeseok Jeon
Department of Electrical & Computer Engineering
Rutgers University

Wednesday, February 22, 2012 - 10:00am - 12:00pm

CoRE Building Lecture Hall

Nano-Relay Technology for Energy-Efficient Electronics

Abstract: As the era of traditional Complementary-Metal-Oxide-Semiconductor (CMOS) technology scaling is coming to an end, continual improvements in integrated-circuit (IC) performance and cost per function are becoming difficult to achieve without increasing power density. This necessitates the investigation of alternate device technologies that surmount the fundamental CMOS energy-efficiency limit and hence enable ultra-low-power ICs. To that end, a nano-electro-mechanical (NEM) relay technology is promising, because of its immeasurably low off-state leakage current and abrupt turn-on behavior, which provide for zero static power consumption and potentially very low dynamic power consumption.

In this talk, I will discuss recent research efforts in NEM relay technology, from both device- and circuit-level perspectives, which led to the successful demonstration of relay-based digital IC building blocks. In addition, I will discuss multi-input relay devices that can lead to smarter design and compact implementation of zero-leakage digital integrated circuits.

Biography: Jaeseok Jeon received the B.A.Sc. degree with first-class honours in electronics engineering from Simon Fraser University, Canada, in 2007 and the Ph.D. degree in electrical engineering from the University of California, Berkeley, in 2011. In 2011, he joined the Rutgers, the State University of New Jersey, as an assistant professor of Electrical and Computer Engineering.

In 2006, he was an electronics designer at Kodak Graphic Communications Canada Company, and he was awarded the 2006 NSERC-USRA award. He was a co-recipient of the 2011 ISSCC Jack Raper Award for Outstanding Technology Directions. His research interests include nano-electro-mechanical relay devices and technology for energy-efficient electronic systems and neural devices and circuits for efficient design of neuromorphic systems.

Dr. Yuanyuan Yang, Dept. ECE, Stony Brook University

Wednesday, December 14, 2011 - 10:00am - 12:00pm

Core Building Lecture Hall


In this talk, we consider a wireless sensor network that consists of a large number of sensors and a limited number of mobile data collectors. In such a network, mobile collectors take over the burden of routing from sensors, roaming over the
sensing area and collecting data from nearby sensors via short-range wireless communications. We present a series of efficient mobile data gathering schemes in such sensor networks, which aim to prolong network lifetime and shorten data gathering latency.

Moving path planning with multi-hop relays. We propose a moving path planning algorithm by adopting a divide and conquer method, which recursively determines a turning point on the path. The moving path of the mobile collector is planed dynamically based on the distribution of sensors, and load balancing among sensors is performed along with the moving path planning to prolong network lifetime.

Single-hop data gathering. To achieve uniform energy consumption among sensors, in this scheme, the mobile collector is scheduled to traverse the transmission range of each sensor such that data from each sensor can be collected via single-hop transmission. However, this approach typically results in significantly increased latency due to the low moving velocity of the mobile collector. Hence, we focus on minimizing the length of a data gathering tour by formulating it into an
optimization problem. A heuristic algorithm is proposed to provide a practically good solution to the problem.

Mobile data gathering with controlled mobility and SDMA technique. In this scheme,we apply the latest physical layer technique, Space-Division Multiple Access (SDMA), to sensor networks, which enables multiple sensors to upload data
simultaneously to the mobile collector so that data uploading time can be greatly shortened. To better enjoy the benefit of SDMA, mobile collector may have to visit some specific locations where more sensors are compatible, which may adversely prolong the moving tour. We propose an optimal solution that minimizes the data gathering latency by exploring a tradeoff between the shortest moving tour and the full utilization of SDMA.

Bounded relay hop mobile data gathering scheme. In this scheme, we study the inherent tradeoff between energy saving and data gathering latency of the mobile data gathering in sensor networks, by achieving a balance between the relay hop count of local data aggregation and the moving tour length of the mobile collector. We propose a polling-based mobile collection approach and formulate it into an optimization problem. Specifically, a subset of sensors are selected as polling points that buffer the locally aggregated data and upload the data to the mobile collector when it arrives. In the meanwhile, when sensors are affiliated with these polling points, it is guaranteed that the relaying of any packet is bounded within a given number of hops.


Dr.Yuanyuan Yang is currently a Full Professor of Electrical & Computer Engineering and Computer Science at Stony Brook University, and the Director of Communications and Devices Division at New York State Center of Excellence in Wireless and Information Technology (CEWIT). She received her PhD degree in computer science from Johns Hopkins University, Baltimore, Maryland, in 1992. Dr. Yang's research interests include interconnection networks, wireless/mobile networks, optical networks, high-speed networks, multicast communication and parallel and distributed
computing systems. She has authored or coauthored more than 230 research articles in leading refereed journals and conferences with over 60 papers published in IEEE Transactions on these topics. She is also an inventor/co-inventor of six U.S. patents in the area of interconnection networks.

Dr. Yang is currently an Associate Editor for the IEEE Transactions on Computers and a Subject Area Editor for the Journal of Parallel and Distributed Computing. She has served as an Associate Editor for IEEE Transactions on Parallel and Distributed Systems. She has served as a distinguished visitor of IEEE Computer Society. She received an IEEE Region 1 Award for ``significant contributions in multicast switching networks'' in 2002, and a Best Paper Award on optical interconnects at the 18th IEEE International Parallel and Distributed Processing Symposium (IPDPS) in 2004. She has served as a general chair, program chair or vice chair for several major conferences and a program committee member for numerous conferences. She was elected as an IEEE Fellow in 2009 "for contributions to parallel and distributed computing systems." More information about her and her research can be found at

Dr. Anant Madabhushi, Dept. of Biomedical Engineering, Rutgers University

Wednesday, November 30, 2011 - 10:00am - 12:00pm


“Quantitative Data Convergence: Fusing radiology, pathology, omics data for predicting disease aggressiveness and patient outcome"

Abstract: Traditional biology generally looks at only a few aspects of an organism at a time and attempts to molecularly dissect diseases and study them part by part with the hope that the sum of knowledge of parts would help explain the operation of the whole. Rarely has this been a successful strategy to understand the causes and cures for complex diseases. The motivation for a systems based approach to disease understanding aims to understand how large numbers of interrelated health variables, gene expression profiling, its cellular architecture and microenvironment, as seen in its histological image features, its 3 dimensional tissue architecture and vascularization, as seen in dynamic contrast enhanced (DCE) MRI, and its metabolic features, as seen by Magnetic Resonance Spectroscopy (MRS) or Positron Emission Tomography (PET), result in emergence of definable phenotypes. At the Laboratory for Computational Imaging and Bioinformatics, we have been developing computerized knowledge alignment, representation, and fusion tools for integrating and correlating heterogeneous biological data spanning different spatial and temporal scales, modalities, and functionalities. The long term research objectives are to explore via efficient computational and pattern recognition methods, the existence and correspondence of biological patterns across heterogeneous data scales and modalities. An understanding of the interplays of different hierarchies of biological information from proteins, tissue, metabolites, and imaging will provide conceptual insights and practical innovations that will profoundly transform people’s lives.

Dr. Irina Rish, IBM Watson


Monday, November 28, 2011 - 10:00am - 12:00pm

Dr. Olgica Milenkovic, Dept ECE, University of Illinois Urbana- Champaign

Wednesday, November 16, 2011 - 10:00am - 12:00pm

Core Building Lecture Hall


Rank aggregation is the problem of combining multiple candidate rankings into one list that best reflects the candidates' standing as a whole. Rank aggregation has many applications, in fields as diverse as bioinformatics, coding theory and social sciences.

Mathematically, the rank aggregation problem can be formulated as finding a permutation that represents the ``centroid'' of a set of permutations - i.e., a permutation that minimizes a given average distance function from the given set of permutations. The main issue arising in such aggregation problems is to identify distance functions that are scalable, flexible and easy to compute.
So far, no solutions that have these desirable properties were proposed.

We introduce a new class of cost-constrained permutation metrics that can be approximated within a constant in polynomial time. The cost functions are based on average transposition distances and for costs that have a tree-metric form, the presented algorithms are exact. To prove the optimality of the distance computation procedure, we use Menger's theorem and graphical representations of permutations.

We conclude the talk by describing a number of applications of the novel distance metric in ``collaborative rank aggregation'' and coding for multilevel flash memories.


Olgica Milenkovic received a MSc degree in mathematics and PhD degree in electrical engineering from the University of Michigan, Ann Arbor, in 2001 and 2002, respectively. From 2002 until 2006 she was with the faculty of University of Colorado, Boulder. In 2006, she was a visiting professor at the University of California, San Diego Center for Information Theory and Application. In 2007, she joined University of Illinois, Urbana-Champaign were she currently holds the position of associate professor. Her research interests are in algorithms, bioinformatics, coding theory, combinatorics and signal processing.

Olgica Milenkovic is a recipient of the NSF Career Award, the DARPA Young Faculty Award and a number of best conference paper awards. She currently serves as Associate editor in the Transactions on Signal Processing and the Transactions on Information Theory.

Dr. Bin Liu, Tsinghua University, China

Friday, November 11, 2011 - 10:00am - 12:00pm

Core Buliding - Lecture Hall


Internet traffic grows exponentially in both the users and the traffic volume, which makes the backbone routers over-sized in the scale of interfaces and over-loaded in the processing power, while the energy consumption grows in a dramatically demand as well. In the traditional routers, the interface part and the packet processing part are one-to-one binding, which makes a low usage for the line-card when traffic is light, leading to high implementation cost, large energy consumption and rigid scalability when scaling to more network interfaces. To reduce the cost, cut down the power consumption and enhance the scalability, in this talk, I propose an architectural innovation for the future router design. I present a new architecture of router which separates router’s line-card into two parts: interface board and processing board. Traffic from all the interfaces is shared by all the processing boards in a router, so we can aggregate the traffic to several a few processing boards and shut down others to save energy when traffic is light. When the aggregated traffic is increasing, the router will wake up the sleeping processing card dynamically and adaptively in an on-demand manner. Given the aggregation of traffic from all ports of a router, there is a small probability that most of them reach up to a peak fluctuation simultaneously, so the entire utilization of processing parts of line cards should be low at most of the time. This gives us a great opportunity to reduce the power consumption of a high speed router.
I this talk, I will briefly describe the architectural innovation, and outline the challenging issues together with the preliminary simulation results.


Bin LIU is a full professor in Tsinghua University since 1999. He received his Ph D degree from Northwestern Polytechnical University, China in1993. His current research areas include high performance switches/routers, network processors, traffic measurement and management, Internet power saving as well as the future Internet. He co-authored the book of “High Performance Switches and Routers” and holds 21 patents in China and abroad. He has served as the TPC Co-Chair for the IWQoS2010, TPC Co-Chair for the Symposium on Next-Generation Networking and Internet Advances in ICC2011, the TPC Co-Chair for the Symposium of IEEE International Conference on Communications in ICC2008, Guest Editor for IEEE Journal on Selected Areas in Communications in 2006. He served as TPC members for many conferences/workshops such as IEEE INFOCOM. He has won a high volume of honors and rewards including the Distinguished Young Scholar of China and the National Top Young Scientist Award, the inaugural Applied Network Research Prize sponsored by IRTF and ISOC in 2011.

Dr. Richard Frenkiel, WINLAB, Rutgers University

Wednesday, November 9, 2011 - 10:00am - 12:00pm

CoRE Building, Board Room 701


Thirty years ago, most large companies had pension plans that promised retirees a "defined" pension-- usually about 1% of a person's final salary for each year worked. Someone with 40 years of service would therefore retire with about 40% of their final salary, plus another 20% or so from Social security. Some pensions even provided cost-of-living adjustments. This allowed a modest lifestyle in retirement or, with some savings, a comfortable and attractive lifestyle. In particular, these plans guaranteed income for life, at a time when people were retiring earlier and living longer.

From the employer's viewpoint, those "defined benefit" plans represented a substantial liability that could escalate sharply in a time of inflation, and as life expectancies increased. As a result, most employers abandoned these "promises to pay" as too expensive and risky in a competitive world. The "defined benefit" pension is now almost extinct, and a successful life now requires some new skills in planning and investing.

Savings plans like the 401K (and other similar retirement savings accounts) are essential to this task. They defer income taxes on "contributions" (money that is put in), and on growth (money that is earned in the account), until the money is removed. Additionally, some employers also "match" a fraction of the employee's contribution. Compared to saving without a 401K, the tax advantage and employer match can easily triple the "effective pension" that can be achieved (the amount of money a person can eventually take out annually in retirement). Even without an employer match, the "effective pension" can be doubled by the delayed tax alone, so there is a strong incentive to participate in whatever type of plan is available. Those who contribute a reasonable amount to these plans, and who invest the money in their plans intelligently, can look forward to retiring relatively early into lives that are interesting, secure and comfortable. Those who fail to contribute, or who invest foolishly, will work longer than they want to and spend their last years in relative poverty. For the new graduate with a first job, these issues may seem far off and more immediate concerns may seem more important. Frequently, action gets postponed, and with each year of delay the desired result becomes harder to achieve.

In this lecture, we will use a simple planning spreadsheet and some basic financial data to explore the issues of how much to save and how to invest those savings. There is no single answer to these questions, of course. Some will wish to retire earlier or live more expensively in retirement. Some will be willing to take greater risks to achieve their goals. The objective of the lecture is for the student to understand what is possible, and how such lifestyle goals are reflected in the overall plan.


Dick Frenkiel was born more than a half-century ago, in a small town called Brooklyn. He attended Tufts University and Rutgers University, emerging with degrees in Mechanical Engineering and a deceptive aura of competence. Soon after joining Bell Laboratories in 1963, he was mistaken for an electrical engineer of similar height and moved into cellular systems engineering, where for sixteen years he did little serious harm.

During the late 1960's. Dick was an author of AT&T's cellular system proposal to the FCC. After an obligatory "growth experience" at corporate headquarters, during which he acquired several suits, he returned to Bell Labs, where he became head of systems engineering for AMPS, the first cellular system in the US. He invented a method for efficient and low-cost cell-splitting, and served on the EIA committee which defined the first cellular standards to be used in the United States.

For Dick, the joy of seeing AT&T's first cellular service in Chicago was somewhat diminished when the company was torn apart by the government and excluded from the cellular business. While not responsible for this calamity, he exiled himself to the lowly world of consumer electronics. He became head of R&D for AT&T's cordless telephone business unit, and led a team that developed cordless telephones, some of which actually worked. He was also responsible for the first manufacture of these telephones in Singapore and Hong Kong, where he acquired several additional suits at attractive prices.

Dick retired from AT&T in 1993, and spent many happy years at WINLAB, the Wireless Information Networks Laboratory here at Rutgers. In 1999, he served as mayor of Manalapan Township in New Jersey and was not indicted. With Professor Narayan Mandayam he teaches a multi-disciplinary course in business strategy in the wireless industry.

For his work in wireless, Dick has received the National Medal of Technology, the Alexander Graham Bell Medal and the Industrial Research Institute Achievement Award. He is a member of the National Academy of Engineering, a New Jersey Inventor of the Year, and a Fellow of Bell Labs and the IEEE.

Dr. Zhi-Quan Luo

Friday, November 4, 2011 - 2:00pm - 4:00pm

CoRE Board Room 701


Consider a multiple input-multiple output (MIMO) interference channel whereby each transmitter and receiver are equipped with multiple antennas. An effective approach to practically achieving high system throughput is to deploy linear transceivers (or beamformers) that can optimally exploit the spatial characteristics of the channel. The recent work of Cadambe and Jafar suggests that optimal beamformers should maximize the total degrees of freedom and achieve interference alignment in the high signal to noise ratio (SNR) regime. In this talk, we examine several issues related to the design of a linear interference alignment scheme including its computational complexity, feasibility and practical algorithms to maximize the channel throughput.


Zhi-Quan (Tom) Luo is a professor in the Department of Electrical and Computer Engineering at the University of Minnesota (Twin Cities) where he holds an endowed ADC Chair in digital technology. He received his B.Sc. degree in Applied Mathematics in 1984 from Peking University, China, and a Ph.D degree in Operations Research from MIT in 1989. From 1989 to 2003, Dr. Luo was with the Department of Electrical and Computer Engineering, McMaster University, Canada, where he later served as the department head and held a senior Canada Research Chair in Information Processing. His research interests lie in the union of optimization algorithms, data communication and signal processing.

Dr. Luo is a fellow of IEEE and SIAM. He is a recipient of the IEEE Signal Processing Society's Best Paper Award in 2004 and 2009, the EURASIP Best Paper Award and the ICC's Best Paper Award in 2011. He was awarded the Farkas Prize from the INFORMS Optimization Society in 2010. Dr. Luo currently chairs the IEEE Signal Processing Society's Technical Committee on Signal Processing for Communications and Networking (SPCOM). He has held editorial positions for several international journals including Journal of Optimization Theory and Applications, Mathematics of Computation, IEEE Transactions on Signal Processing, SIAM Journal on Optimization, Management Sciences and Mathematics of Operations Research.

Dr. Wei Jiang, Rutgers University, Dept. of Electrical & Computer Engineering

Wednesday, November 2, 2011 - 10:00am - 12:00pm

CoRE Board Room 701


Silicon photonics offers a low-cost platform for building large-scale photonic devices and circuits, potentially replicating the success of silicon microelectronics in photonics. Photonic crystal nanostructures provide novel physical mechanisms, such as the slow-light effect and superprism effect, to reduce the size and improve the performance of silicon photonic devices. This talk will review our recent work on silicon-compatible waveguides, including slow-light photonic crystal waveguides, for use as on-chip optical delay lines. Particularly, some fundamental physical issues of photonic crystal waveguides such as slow-light loss will be addressed. Optical delay lines have applications in phased array antennas, optical signal processing, and optical modulation. It will be shown that the current slow-light loss levels are sufficient for some applications, whereas challenges remain in other applications. This talk will also review our work on dual racetrack silicon micro-resonators for quadrature amplitude modulation. Potential applications of these device components in optical communications, optical interconnects, laser beam steering, and optical signal processing will be discussed.

Bio sketch

Wei Jiang is an assistant professor in the Department of Electrical and Computer Engineering of Rutgers, the State University of New Jersey. He received his B.S. degree in physics from Nanjing University, Nanjing, China, in 1996, and his M.A. degree in physics and his Ph.D. degree in electrical and computer engineering from the University of Texas, Austin, in 2000 and 2005, respectively. His research interests encompass silicon photonics, photonic crystals, nanophotonics, and their applications in various optoelectronic systems.

Dr. Jeyanandh Paramesh, Carnegie Mellon University

Wednesday, October 19, 2011 - 10:00am - 12:00pm

CoRE Board Room 701

The mm-wave frequency bands offer enormous potential for multi-Gb/s communications as well as emerging imaging and ranging applications. The realization of this potential will be underpinned by the development of high-performance, power-efficient transceivers in nanoscale CMOStechnologies. Two key challenges must be met towards achieving this goal. First, the mm-wave front-end circuits must be designed to operate over extremely wide bandwidths of several tens of GHz, both to exploit the large bandwidth availability, and also to provide sufficient margins to tolerate process, voltage and temperature variations that are increasingly problematic in nanoscale CMOS. Second, reducing power consumption in the front-end is imperative especially since phased-arrays are mandated in mm-wave transceivers. This talk presents circuit solutions to the aforementioned challenges. We introduce design approaches to the unilateralization of common-source and common-gate gain amplifiers, of both the narrowband and ultra-wideband varieties. We then present design techniques for mm-wave voltage-controlled oscillators that tune over several tens of GHz. These techniques are demonstrated through several CMOS prototypes operating in the 24 GHz and 60 GHz bands.


Jeyanandh Paramesh received the B.Tech, degree from IIT, Madras, the M.S degree from Oregon State University and the Ph.D. degrees from the University of Washington, Seattle, all in Electrical Engineering. He is currently Assistant Professor of Electrical and Computer Engineering at Carnegie Mellon University. He has held product development positions with Analog Devices, where he designed high-performance data converters, and Motorola where he designed analog and RF integrated circuits for cellular transceivers. From 2002 to 2004, he was a graduate student researcher with the Communications Circuit Lab, Intel where he developed multi-antenna receivers, high-efficiency power amplifiers and high-speed data converters high data-rate wireless transceivers. His research interests include the design of RF and mixed-signal integrated circuits and systems for a wide variety of applications

Dr. Jeong Bong Lee
University of Texas at Dallas

Location: Computer Science Seminar Room CoRE-301

Friday, October 14, 2011 - 10:00am - 12:00pm

Computer Science Seminar Room CoRE-301

Photonic crystals are ultra-compact highly integrated nano scale structures which demonstrated the possibility of generating, manipulating, processing, transmitting and detecting light. However, most photonic crystal devices are “passive” structures without any means of external on-demand control. Adding tunability to the photonic crystals would greatly expand their application areas and enable unforeseen new application areas. MEMS/NEMS technologies are ideally positioned to provide a wide variety of unprecedented radical options of tunability to the passive photonic crystal devices as they can be co-integrated with photonic crystals. We have recently reported MEMS-enabled mechanically tunable and thermally tunable photonic crystals. Various applications and current status of development of such tunable photonic crystals will be discussed.

Speaker Bio:

Dr. Jeong-Bong (JB) Lee received the Ph.D. degree from Georgia Tech, Atlanta, Georgia in 1997. In 2001, he joined the Electrical Engineering Department at UTD where he is now a full professor. His current research interests include MEMS and nanophotonics for biomedical and photonics applications. He received the NSF CAREER AWARD in 2001. He is currently serving as an editorial board member for Micromachines journal. He has served as a program committee member for many international conferences including Transducers 2011 Conference as a member of executive program sub-committee. He also served as a member of external advisory board for the Microsystems division at the Sandia National Laboratories in 2007. He has five U.S. patents, more than 45 journal papers and 138 conference papers published.

Dr. K. Venkatesh Prasad
Ford Motor Company

Wednesday, September 28, 2011 - 10:00am - 12:00pm

CoRE Building Auditorium

Automobiles as Technology Platforms for a Personal Mobility Experience and a Better World

With more than a billion cars, trucks and buses on this planet and a relentless growth to the second billion, there is an immense and immediate opportunity for us all in the public, academic and private sectors to come together to make a lasting difference.

Automobiles pose a number of design challenges: they must jointly serve the desires of the consumer and the demands of society; they need to have an emotive appeal and yet must last at least 10 years or 150,000 miles, as a tightly regulated product; they are simultaneously complex cyber-physical systems and components of a much larger system-of-systems. Modern automobiles have transformed themselves into technology platforms to address these challenges and yet there is need to do much more so they can actively help reduce congestion, avoid or mitigate accidents, reduce fuel consumption, while offering all the conveniences of being digitally connected such as being able to call, hands-free, find parking and park, and being easily reprogrammable, to be rented or shared with little human intermediation.

The purpose of this talk is to share the excitement of designing automobiles as information and communication technology platforms and to stimulate a discussion of related cross-disciplinary collaboration opportunities.

Speaker Bio:

Dr. K. Venkatesh Prasad (class of 1990) is group and senior technical leader of Vehicle Design and Infotronics for Ford Research and Innovation. He is member of Ford’s 12-person global Technology Advisory Board, chaired by the CTO. Dr. Prasad is responsible for the research, architecture, standards, applications development and vehicle system integration of electrical, electronics and embedded software technologies.

Before joining Ford Motor Company in 1996, Prasad worked as a senior scientist at RICOH Innovations in Menlo Park, Calif., developing automatic "lip reading" as a novel human-machine interface. In addition, he was at Caltech and the NASA Jet Propulsion Laboratory in Pasadena, Calif., where he worked on the world's first telerobotic visual surface inspection system to help design the International Space Station.

Attracted by an open-ended challenge to discover ways to integrate "intelligence" into cars and trucks, Prasad joined Ford to work with a small group of engineers in the development of adaptive headlamp and lane-mark detection technologies.

Professor Adrian Perrig
Carnegie Mellon University

Tuesday, May 3, 2011 - 10:00am - 12:00pm

CoRE Auditorium

"SCION: Scalability, Control, and Isolation on Next-Generation Networks" by Prof. A. Perrig, Carnegie Mellon University

We present the first Internet architecture designed to provide route control, failure isolation, and explicit trust information for end-to-end communications. SCION separates ASes into groups of independent routing sub-planes, called trust domains, which then interconnect to form complete routes. Trust domains provide natural isolation of routing failures and human misconfiguration, give endpoints strong control for both inbound and outbound traffic, provide meaningful and enforceable trust, and enable scalable routing updates with high path freshness. As a result, our architecture provides strong resilience and security properties as an intrinsic consequence of good design principles, avoiding piecemeal add-on protocols as security patches. Meanwhile, SCION only assumes that a few top-tier ISPs in the trust domain are trusted for providing reliable end-to-end communications, thus achieving a small Trusted Computing Base. Both our security analysis and evaluation results show that SCION naturally prevents numerous attacks and provides a high level of resilience, scalability, control, and isolation.

Adrian Perrig is a Professor in Electrical and Computer Engineering, Engineering and Public Policy, and Computer Science at Carnegie Mellon University. Adrian serves as the technical director for Carnegie Mellon's Cybersecurity Laboratory (CyLab). He earned his Ph.D. degree in Computer Science from Carnegie Mellon University, and spent three years during his Ph.D. degree at the University of California at Berkeley. He received his B.Sc. degree in Computer Engineering from the Swiss Federal Institute of Technology in Lausanne (EPFL). Adrian's research revolves around building secure systems and includes network security, trustworthy computing and security for social networks. More specifically, he is interested in trust establishment, trustworthy code execution in the presence of malware, and how to design secure next-generation networks.

Dr. Patrick Flandrin
Department of Ecole Normale Superieure de Lyon, France

Friday, April 29, 2011 - 2:00pm - 3:00pm

7th Floor Board Room, CoRE Building

The concept of stationarity is revisited from an operational perspective that explicitly takes into account the observation scale. A general, time-frequency-based, framework is described for testing such a relative stationarity via the introduction of stationarized surrogate data. Two variations are discussed, based on either dissimilarity measures between local and global spectra, or machine learning approaches. Different extensions, including wavelet-based tests for images and transient detection, are considered.

Patrick Flandrin is a CNRS Senior Researcher, working with the "Signals, Systems and Physics" Group in the Physics Department of Ecole Normale Supérieure de Lyon, France. His research interests are mostly in nonstationary signal processing (with emphasis on time-frequency methods and wavelets), scaling processes and complex systems. He authored numerous publications in those areas over the last 30 years, including the book "Time-Frequency/Time-Scale Analysis" (Academic Press, 1999). Dr Flandrin has been awarded the Philip Morris Scientific Prize in 1991, the SPIE Wavelet Pioneer Award in 2001 and the Prix Michel Monpetit from the French Academy of Sciences in 2001. Fellow of IEEE (2002) and EURASIP (2009), he has been elected Member of the French Academy of Sciences in 2010.

Professor Milica Stojanovic
Northeastern University

Wednesday, April 27, 2011 - 10:00am - 12:00pm

CoRE Auditorium

"Underwater Sensor Networks: Random Access and Compressive Sensing" by M. Stojanovic, Northeastern University

Wireless information transmission through the ocean is one of the enabling technologies for the development of future ocean-observation systems, whose applications range from oil industry to aquaculture and include gathering of oceanographic data, pollution control, climate recording, and transmission of images from remote sites. Underwater wireless communications are usually established using acoustic waves, since electro-magnetic waves propagate only over very short distances. However, acoustic communications are limited by three factors: low bandwidth, low speed of sound, and poor quality of the physical link. These constraints yield a difficult communication channel, while additional, system-level constraints come from the limited battery supply (re-charging is difficult underwater) and the half-duplex operation of existing acoustic modems. For networks that are deployed for long-term monitoring of environmental phenomena, it is crucial to design an efficient data gathering scheme that prolongs the system’s life-time. To this end, we exploit the sparse nature of the monitored field and design a random access / compressive sensing (RACS) scheme in which the sensors transmit at random to a fusion center which then reconstructs the field. We provide an analytical framework for system design that captures packet collisions due to random access, as well as packet errors due to communication noise. Through analysis and examples, we demonstrate that recovery of the field can be attained using only a fraction of the resources (energy per bit, bandwidth) used by a conventional TDMA network, while employing a scheme that is simple to implement, requires no synchronization or scheduling, and no downlink feedback.

Milica Stojanovic graduated from the University of Belgrade, Serbia, in 1988, and received the M.S. and Ph.D. degrees in electrical engineering from Northeastern University, Boston, MA, in 1991 and 1993. After a number of years with the Massachusetts Institute of Technology, where she was a Principal Scientist, she joined the faculty of Electrical and Computer Engineering Department at Northeastern University in 2008. She is also a Guest Investigator at the Woods Hole Oceanographic Institution, and a Visiting Scientist at MIT. Her research interests include digital communications theory, statistical signal processing and wireless networks, and their applications to underwater acoustic communication systems. Milica is a Fellow of the IEEE and serves as an Associate Editor for the IEEE Journal of Oceanic Engineering and the IEEE Transactions on Signal processing.

Link to Dr. Stojanovic's presentation slides.

Professor Nader Engheta
University of Pennsylvania

Tuesday, April 5, 2011 - 10:00am - 12:00pm

Core Auditorium

In recent years, in my group we have been working on various aspects of metamaterials and plasmonic nano-optics. We have introduced and been developing the concept of “metatronics”, i.e. metamaterial-inspired optical nanocircuitry, in which the three fields of “electronics”, “photonics” and “magnetics” can be brought together seamlessly under one umbrella – a paradigm which I call the “Unified Paradigm of Metatronics”.

In this novel optical circuitry, the nanostructures with specific values of permittivity and permeability may act as the lumped circuit elements such as nanocapacitors, nanoinductors and nanoresistors. Nonlinearity in metatronics can also provide us with novel optical nonlinear lumped elements. We have investigated the concept of metatronics through extensive analytical and numerical studies, computer simulations, and recently in a set of experiments at the IR wavelengths.

We have shown that nanorods made of low-stressed Si3N4 with properly designed cross sectional dimensions indeed function as lumped circuit elements at the IR wavelengths between 8 to 14 microns. We have been exploring how metamaterials can be exploited to control the flow of photons, analogous to what semiconductors do for electrons, providing the possibility of one-way flow of photons. We are now extending the concept of metatronics to other platforms such as graphene, which is a single atomically thin layer of carbon atoms, with unusual conductivity functions. We study the graphene as a new paradigm for metatronic circuitry and also as a “flatland” platform for IR metamaterials and transformation optics, leading to the concepts of one -atom-thick metamaterials, and one-atom-thick circuit elements and optical devices. I will give an overview of our most recent results in these fields.

This ECE Colloquium will be held in the CoRE Building Auditorium on Busch Campus.

Flyer - Nader Engheta

Professor Philippe M. Fauchet
University of Rochester

Wednesday, March 23, 2011 - 10:00am - 12:00pm

CoRE Lecture Hall

Flyer - Philippe M. Fauchet

Dr. Ali H. Sayed
University of California Los Angeles

Monday, March 21, 2011 - 10:00am - 12:00pm

CoRE Auditorium

Adaptive networks consist of spatially distributed agents that are linked together through a connection topology. The topology may vary with time and the agents may also move. The agents cooperate with each other through local interactions and by means of in-network processing. The diffusion of information across the network results in various forms of self-organizing behavior and collective intelligence. A key property of adaptive networks is that all agents behave in an isotropic manner and are assumed to have similar abilities. This kind of behavior is common in many socio-economic and life and biological networks where no single agent is in command. Adaptive networks are well-suited to perform decentralized information processing and decentralized inference tasks. They are also well-suited to model self-organizing behavior such as animal flocking and swarming. This talk describes research results on distributed processing over adaptive networks and illustrates the techniques by studying self-organization in biological networks such as bird formations, fish schooling, bee swarming, and bacteria motility.

A H. Sayed is Professor of Electrical Engineering and Director of the UCLA Adaptive Systems Laboratory. His research areas span adaptation and learning mechanisms, adaptive and cognitive networks, bio-inspired information processing, distributed and statistical signal processing. He has published 5 books and over 350 articles. His work received several awards including the 1996 IEEE Fink Prize, the 2003 Kuwait Prize, the 2005 Terman Award, and two Best Paper Awards from the IEEE Signal Processing Society (2002, 2005). He is a Fellow of IEEE and served as Editor-in-Chief of the IEEE Transactions on Signal Processing (2003-2005) and as 2005 Distinguished Lecturer of the IEEE SP Society. He is currently serving as Vice-President of Publications of the same Society.

Flyer - Ali Sayed

Professor Stojanovic
Northeastern University

Tuesday, March 15, 2011 - 12:23pm - Wednesday, March 16, 2011 - 12:23pm

CoRE 701 Boardroom

Professor Youngmoo Kim
Drexel University

Wednesday, March 2, 2011 - 10:00am - 12:00pm

CoRE Building room 301


Recent advances in signal processing, sensing, and computing have facilitated the development of new technologies with the potential for enhancing musical expression, interaction, and education. This presentation will highlight research by the Music & Entertainment Technology Laboratory (MET-lab) at Drexel University exploring music, emotion, and creative expression under the common vision of making music more interactive and accessible for both musicians and non-musicians. These projects encompass the recognition of emotion, such as a system for dynamic musical mood prediction and a collaborative web game for the collection of emotional annotations, as well as interfaces for expressive performance, including a novel electromagnetic approach to shaping the sound of the acoustic piano and a user-friendly controller for remixing music in terms of emotion. Recent work has also used humanoid robots to explore aspects of musical instrument performance and understanding. These and other MET-lab efforts are closely coupled with educational initiatives, many of which have been deployed in K-12 outreach programs in the Philadelphia region, to promote learning in Science, Technology, Engineering, and Mathematics (STEM).
- - - - - - - - -


Youngmoo Kim is an Assistant Professor of Electrical and Computer Engineering at Drexel University. His research group, the Music & Entertainment Technology Laboratory (MET-lab) focuses on the machine understanding of audio, particularly for music information retrieval. Other areas of active research at MET-lab include analysis-synthesis of sound, human-machine interfaces and robotics for expressive interaction, and K-12 outreach for engineering, science, and mathematics education. Youngmoo received his Ph.D. from the MIT Media Lab in 2003 and also holds Masters degrees in Electrical Engineering and Music (Vocal Performance Practice) from Stanford University. He served as a member of the MPEG standards committee, contributing to the MPEG-4 and MPEG-7 audio standards, and he co-chaired the 2008 International Conference on Music Information Retrieval (hosted at Drexel). His research is supported by the National Science Foundation and the NAMM Foundation, including an NSF CAREER award in 2007.

Refreshments will be served.

This is part of the ECE Colloquium Series. Please contact Professor Yanyong Zhang (yyzhang [at] winlab [dot] rutgers [dot] edu) for further information.

Flyer - Youngmoo Kim

Dr. Thomas Schneider
National Institutes of Health
Frederick, Maryland

Wednesday, February 23, 2011 - 1:00pm - 3:00pm

CoRE Lecture Hall

Thomas D. Schneider, Ph.D.
National Cancer Institute at Frederick
Gene Regulation and Chromosome Biology Laboratory
Molecular Information Theory Group

Wednesday, February 23, 2011 - 12:25pm

Core Auditorium

Professor Paul Oh
Drexel University

Wednesday, February 16, 2011 - 2:00pm - 4:00pm

CoRE Lecture Hall

Flyer - Paul Oh

Professor Tsuhan Chen
Cornell University, Dept ECE

Monday, December 6, 2010 - 2:00pm - 4:00pm

CoRE Lecture Hall

Flyer - Tsuhan Chen

Dr. Hubertus Franke
IBM T.J. Watson Research Center

Wednesday, December 1, 2010 - 10:00am - 12:00pm

CoRE Lecture Hall

Flyer - Hubertus Franke

Professor Xiaodong Wang
Columbia University

Wednesday, October 20, 2010 - 10:00am - 12:00pm

CoRE Lecture Hall

Flyer - Xiaodong Wang

Dr. Sharad Mallik, Chair, Princeton University

CoRE Building Lecture Hall

Title:  Lessons from Formal Hardware Verification

Abstract:   Formal system verification aims to provide mathematical proofs of correctness starting with a system model and correctness properties of interest. This offers completeness guarantees that are missing in testing based debugging approaches. However, it faces significant challenges in building faithful system models, and in scaling the proofs to system sizes of practical interest. Nonetheless, over the past couple of decades formal verification has seen increasing practical use in hardware design, and to a lesser extent in software systems. In this talk I will share the main lessons on what has enabled formal hardware verification thus far. In this context, I will cover the enabling role of SAT and SMT solvers, proofs using model checking, and the value of appropriate abstractions.


Sharad Malik is the George Van Ness Lothrop Professor of Engineering at Princeton University and the Chair of the Department of Electrical Engineering. He has served as the Director of the multi-university MARCO Gigascale Systems Research Center (GSRC, 2009-2012), and as the Associate Director of the Center for Future Architectures Research (C-FAR, 2013-2016).

His research focuses on design methodology and design automation for computing systems. His research in functional timing analysis and propositional satisfiability has been widely used in industrial electronic design automation tools.

He has received the DAC Award for the most cited paper in the 50-year history of the conference (2013), the CAV Award for fundamental contributions to the development of high-performance Boolean satisfiability solvers (2009), the ICCAD Ten Year Retrospective Most Influential Paper Award (2011) as well as other research and teaching awards. He is a fellow of the IEEE and ACM.