Assistant Professor Mehdi Javanmard is the recipient of a new NSF award for the project titled "A Microfluidic-CMOS Cross-cut Approach Enabling Tri-Modal Biorecognition for Highly Accurate Viral Diagnostics." This is a three year $450,000 collaborative effort between Rutgers University (<b>Mehdi Javanmard</b>, PI) and Princeton University (<b>Kaushik Sengupta</b>, PI). Rutgers' share for this award is $225,000.
As part of this project, Mehdi and his team will develop a finger-stick sized instrument whose purpose is to rapidly diagnose viral infections in blood. The proposed point-of-use device can be utilized for rapidly screening subjects at airports, emergency rooms, or other crowded areas where the potential to spread viral disease is high. The proposed innovation is based on miniaturization of sample, reagent, and buffer handling in microfluidics using low power electronically actuated micro-valves, reconfigurable electroosmotic pumps, and multiplexed detection of fluorescence-labeled proteins and nucleic acids in silicon ICS with integrated nanoplasmonic filters that remove the necessity of complex optical scanners, lenses, collimators. The platform is envisioned to be generic and reconfigurable and the pre-functionalized cartridges can be swapped out for different infectious diseases. Specifically, the proposed research aims to investigate and develop multi-modal detection capability through electronically actuated fluidic valves and pumps enabling on-chip immunoassays for protein detection and on-chip nucleic acid purification, amplification, and hybridization for viral load determination as well as light guiding, packaging and additive manufacturing techniques for enabling a sample-to-answer platform.
Congratulations on this success, Mehdi!
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.