Sparse and Low Rank Methods for Imbalanced and Heterogeneous Data

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.

The ProteOhmic Smart-Patch: Transcutaneous Monitoring of Molecular Levels in Blood Using Flexible and Natural Substrates

Mehdi Javanmard received a DARPA grant for the project "The ProteOhmic Smart-Patch: Transcutaneous Monitoring of Molecular Levels in Blood Using Flexible and Natural Substrates". The award amount is $185,887.15 and the duration is 15 months.

FIA-NP: Collaborative Research: The Next-Phase MobilityFirst Project - From Architecture and Protocol Design to Advanced Services and Trial Deployments

Principal Investigators: Dipankar Raychaudhuri, Roy D. Yates, Yanyong Zhang, Wade K. Trappe, Richard P. Martin

NSF has processed supplemental funding in the amount of $598,451 for the referenced award. The award, with this amendment, now totals $2,899,858.

The ThruProt Analyzer: Bringing Proteomics to the Field Using a Sample-to-Answer Electronic Multiplexed Platform

Mehdi Javanmard received a $344,942 NSF grant for the project "The ThruProt Analyzer: Bringing Proteomics to the Field Using a Sample-to-Answer Electronic Multiplexed Platform". This is a 3-year collaborative grant with colleagues from the RU Ocean Sciences Department, and Mehdi is the PI.

The abstract of the award is given below.

"IDBR: TYPE A- The ThruProt Analyzer: Bringing Proteomics to the Field Using a Sample-to-Answer Electronic Multiplexed Platform".

PI: Mehdi Javanmard,

Structured Sparse and Low-Rank Representations for Multimodal Recognition

We are pleased to announce that ECE Assistant Professor Vishal Patel is recipient of the 2016 Young Investigator Program (YIP) Award.

The goal of Dr. Patel’s project is to develop robust and efficient methods for learning structured representations of multimodal data. In particular, he will develop methods for multimodal metric learning and multimodal data fusion based on sparse and low-rank representations.

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