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.
Shantenu Jha is a co-PI and the lead Computing Engineer/Science Faculty on a new $20M NSF Scientific Software Innovation Institutes for Molecular Sciences. The Institute called MolSSI -- Molecular Science Software Institute, will enable computational scientists to tackle problems that are significantly larger and more complex than thosecurrently within reach. MolSSI will impact domains that use molecular simulations -- from proteins and drug designs to the next generation of materials.
MolSSI is one of only two Software Institutes funded by the NSF and represents some of the most innovative and daring investment in cyberinfrastructure in decades by the NSF. As part of this initiative, Prof. Jha will be responsible for the (i) software engineering process, (ii) middleware upon which MolSSI supported science and codes will depend, and (iii) deployment and integration with NSF production infrastructure.
For more information please see the press release from the NSF at
Professors Anand Sarwate (PI) and Narayan Mandayam (co-PI) have received a new NSF grant from the Secure and Trustworthy Cyberspace (SaTC) program.
The title of the project is "PERMIT: Privacy-Enabled Resource Management for IoT Networks” and the award amount is $500,000 for three years (9/1/16 to 8/31/19).
The project investigates how privacy can be used to inform the design and management of future data sensing systems involved in applications related to the Internet of Things (IoT), industrial monitoring and control, "smart" homes/cities, and personalized health care. These systems will collect sensitive information about individuals and also require high bandwidth. For applications or services that rely on populations of individuals, reducing the amount of information transmitted can save bandwidth while enhancing privacy. The objectives of this work are to use ideas from data privacy technologies and wireless resource management techniques to jointly manage privacy and bandwidth in wireless sensing systems. Ultimately, this project seeks to formulate new trade-offs between privacy and quality-of-service that can generalize to other networking problems.
The full project abstract can be found on the NSF website:
Vishal M. Patel received an NSF award for the project titled "Sparse and Low Rank Methods for Imbalanced and Heterogeneous Data." This is a two year collaborative effort between Rutgers University (Vishal Patel, PI) and The Johns Hopkins University. Rutgers' share for this award is $249,152.
As part of this project, Vishal and his team will develop novel sparse and low-rank modeling techniques for dealing with imbalanced, heterogeneous and multi-modal visual data-sets.
Abstract: In rcent years, sparse and low-rank modeling techniques have emerged as powerful tools for efficiently processing visual data in non-traditional ways. A particular area of promise for these theories is visual recognition, where object detection and image classification approaches need to be able to deal with the highly diverse appearance of real-world objects. However, existing visual recognition methods generally succeed only in the presence of sufficient amounts of homogeneous and balanced training data that are well matched to the actual test conditions. In practice, when the data are heterogeneous and imbalanced, the performance of existing methods can be much worse than expected.
This project will develop a comprehensive framework for real-world visual recognition based on novel sparse and low-rank modeling techniques, which will be able to deal with imbalanced, heterogeneous and multi-modal data. Imbalanced data will be handled using convex optimization techniques that automatically divide a dataset into common and rare patterns, and select a small set of representatives for the common patterns that are then combined with the rare patterns to form a balanced dataset. Heterogeneous and multi-modal data will be handled using non-convex optimization techniques that learn a latent representation from multiple domains or modalities. Classification and clustering algorithms can be applied to the latent representation. Applications of these methods include image and video-based object recognition, activity recognition, video summarization, and surveillance.
For more information, please see: http://www.nsf.gov/awardsearch/showAward?AWD_ID=1618677&HistoricalAwards...