The Board of Governors has approved Dr. Athina Petropulu's promotion to Distinguished Professor effective July 1, 2017. Congratulations on this well deserved accomplishment Athina!
Athina P. Petropulu received her undergraduate degree from the National Technical University of Athens, Greece, and the M.Sc. and Ph.D. degrees from Northeastern University, Boston MA, all in Electrical and Computer Engineering. She is Professor at the Electrical and Computer Engineering (ECE) Department at Rutgers, having served as chair of the department during 2010-2016. Before joining Rutgers in 2010, she was faculty at Drexel University. She held Visiting Scholar appointments at SUPELEC, Universite' Paris Sud, Princeton University and University of Southern California.
Dr. Petropulu's research spans the area of statistical signal processing and wireless communications. She has made fundamental contributions in the area of cooperative approaches for wireless communications, physical layer security, MIMO radars using sparse sensing, and blind system identification using higher-order statistics. Her research has been funded by various government industry sponsors including the National Science Foundation, the Office of Naval research, the US Army, the National Institute of Health, the Whitaker Foundation, Lockheed Martin and Raytheon.
Dr. Petropulu is Fellow of IEEE and recipient of the 1995 Presidential Faculty Fellow Award given by NSF and the White House. She has served as Editor-in-Chief of the IEEE Transactions on Signal Processing, IEEE Signal Processing Society Vice President-Conferences and member-at-large of the IEEE Signal Processing Board of Governors. She was the General Chair of the 2005 International Conference on Acoustics Speech and Signal Processing (ICASSP-05), Philadelphia PA, and is General co-Chair of the 2018 IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Kalamata, Greece. In 2005 she received the IEEE Signal Processing Magazine Best Paper Award, and in 2012 the IEEE Signal Processing Society Meritorious Service Award for "exemplary service in technical leadership capacities". She is currently IEEE Distinguished Lecturer for the Signal Processing Society. In 2016 she served as president of the ECE Department Heads Association (ECEDHA).
More info on her work can be found at www.ece.rutgers.edu/~cspl
ECE Assistant Professor Mehdi Javanmard's work on nanoelectronic barcoding for health monitoring is featured on the cover of the Royal Society of Chemistry journal Lab on a Chip and is featured here in Rutgers Today. He and his graduate student Pengfei Xie have developed a biosensor – known as a lab on a chip – that could be used in hand-held or wearable devices to monitor health and exposure to dangerous bacteria, viruses and pollutants. Their work has also received attention in other media outlets, including the Huffington Post and others (see examples below). Congratulations Mehdi.
Dean Thomas Farris has just announced that Distinguished Professor Dipankar Raychaudhuri will receive the 2017 School of Engineering (SoE) Faculty of the Year Award. This award recognizes exceptional contributions of a SoE faculty member to the School of Engineering, the University, the engineering profession, the scientific community and/or society at large. Ray will be recognized with this award at a SoE Faculty Recognition Event on September 14 at 4 pm, where he will receive a plaque and a monetary award in the amount of $5,000 to be used to support his continued research and scholarship activities. This a well deserved recognition for Ray's continued excellence in the leadership of WINLAB and the prominence it brings to ECE. Congratulations Ray!
Professor Maryam Mehri Dehnavi is the PI on a new NSF grant entitled "Performance-in-Depth Sparse Solvers for Heterogeneous Parallel Platforms." This is a two year project totaling $175,000 and is supported under the Computer and Information Science and Engineering (CISE) Research Initiation Initiative (CRII).
The project conducts an in-depth investigation of performance bottlenecks in sparse solvers and reformulates their standard variants to deliver end-to-end performance. Cross-layer solutions are developed to improve data locality, reduce communication, and increase inherent parallelism in sparse linear solvers.
The solutions involve multi-level algorithm restructuring and performance tuning to significantly improve the scalability and performance of sparse computations while preserving their numerical accuracy, convergence, and stability. The proposed methods and algorithms are implemented as domain-specific high-performance software and a benchmark suite to promote iterative improvements of the developed algorithms and codes.
Professor Shantenu Jha is the lead PI on a 3 year NSF award for $1.25M on a project titled "The Power of Many: Ensemble Toolkit for Earth Sciences." This is a three way collaborative project between Rutgers, Penn State University and Princeton. In this project, Dr. Jha will work with Michael Mann (https://en.wikipedia.org/wiki/Michael_E._Mann) a distinguished Climate Scientist at Penn State and Guido Cervone to advance high-performance computing based methods for the analysis of CMIP5 data. Dr. Jha will also work with Jeroen Tromp and others at Princeton to help advance computational modelling capabilities of Seismic Inverse Problems and thus seismic hazard assessment. This award is funded as part of the NSF EarthCube Program which is a joint solicitation between Advanced Cyberinfrastructure and Geosciences.
Please see below an abstract for the project:
The Power of Many: Ensemble Toolkit for Earth Sciences
Abstract: The study of hazards and renewable energy are paramount for the development and sustainability of society. Similarly, the emergence of new climatic patterns pose new challenges for future societal planning. Geospatial data are being generated at unprecedented rate exceeding our analysis capabilities and leading towards a data-rich but knowledge-poor environment. The use of advanced computing tools and techniques are playing an increasingly important role in contributing to solutions to problems of societal importance. This project will create specialized computational tools that will enhance the ability of scientists to effectively and efficiently study natural hazards and renewable energy. The use of these tools will support novel methods and the use of powerful computing resources in ways that are not currently possible.
Many scientific applications in the field of Earth Sciences are increasingly reliant on “ensemble-based” methods to make scientific progress. This is true for applications that are both net producers of data, as well as aggregate consumers of data. In response to the growing importance and pervasiveness of ensemble-based applications and analysis, and to address the challenges of scale, simplicity and flexibility, we propose the Ensemble Toolkit for Earth Sciences. The Ensemble Toolkit will provide an important addition to the set of capabilities and tools that will enable the Earth Science community to use high-performance computing resources more efficiently, effectively and in an extensible fashion.
This project represents the co-design of Ensemble Toolkit for Earth Sciences and is a collective effort of an interdisciplinary team of cyberinfrastructure and domain scientists. It will also support the integration of the Ensemble Toolkit with a range of science applications, as well as its use in solving scientific problems of significant societal impact that are currently unable to utilize the collective capacity of supercomputers, campus clusters and clouds.
Professor Laleh Najafizadeh is part of a team receiving a grant from the New Jersey Commission on Brain Injury Research. The title of the project is "Role of Cortical Network Plasticity in Recovery from Traumatic Brain Injury" and the award amount is $540,000. This project is in collaboration with Professor David Margolis (PI) from the Department of Cell Biology and Neuroscience (CBN) in the School of Arts and Sciences at Rutgers, and Professor Janet Alder from the Department of Neuroscience and Cell Biology at Rutgers Robert Wood Johnson Medical School.
The project uses a mouse model along with novel imaging and quantitative methods to investigate brain plasticity underlying recovery from traumatic brain injury, and to screen candidate drugs with potential therapeutic value for treating traumatic brain injury. There is a critical need to discover treatments that improve recovery of brain function after injury. Little is known about how brain activity changes in response to the initial injury and how these changes cause lasting detrimental effects on mental function and behavior. The proposed experiments and analytical methods aim to identify the mechanisms and timing of the brain’s recovery from injury and to relate brain changes in the same subjects to cognitive and behavioral recovery.
Professor Laleh Najafizadeh is PI on a new NSF grant from the Biomedical Engineering (BME) Program of the Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET).
The title of the project is "Probing Neural Connectivity at Multiple Temporal Scales" and the award amount is $363,273. This project is in collaboration with David Margolis (Co-PI), Assistant Professor at the Department of Cell Biology and Neuroscience (CBN) from the School of Arts and Sciences at Rutgers.
This project will develop a new comprehensive framework that will enable quantitative assessment of studying short-term and long-term network changes to advance our understanding of the dynamics of functional reorganization of the brain. The brain is a highly complex dynamic system in which neural functional connections are continuously changing at multiple time scales. These changes can occur at short scales, for example due to learning a simple task, or at relatively longer scales, due to wide range of reasons, such as learning complex concepts, brain-related diseases, and going through rehabilitation. Currently, our understanding of how the neural functional interactions form and change with time has been very limited because of lack of 1) quantitative measures that can reliably characterize these changes at different time scales, and 2) the ability to continuously monitor and record brain activities at different time scales, from millisecond to days and weeks. This project aims to address these limitations by taking a combined theoretical-experimental approach to establish a comprehensive data-driven framework that will enable quantitative characterization of the dynamic properties of brain functional networks at multiple temporal scales. With a focus on somatosensory learning, this research will use the proposed framework along with chronic imaging in GCaMP6f reporter mice, to quantitatively examine 1) how the interactions among functional brain networks are modified by task performance (short term changes), 2) how such interactions differ over days when mice finally becomes expert in performing the task (long term changes), and 3) how manipulating different nodes in the network, will change the dynamics of brain functional interactions. The success of this project, by including the temporal dimension into the analysis, will have a transformative impact in the field of neuroscience. This project will also provide a unique opportunity for the graduate and undergraduate students to obtain multidisciplinary expertise at the intersection of signal processing, statistics, neurobiology and imaging, thus providing an ideal platform for the training of the next generation engineers and neuroscientists.