PEOPLE 2017-05-09T15:54:06+00:00

Prof. Waheed U. Bajwa

Assistant Professor
Department of Electrical and Computer Engineering
Rutgers, The State University of New Jersey

Research Interests

High-dimensional inference and inverse problems, geometrical methods for “big data” analytics, sampling theory, statistical signal processing, machine learning, wireless communications, and applications in biological sciences, complex networked systems, and radar & image processing.

Biography

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, geometrical methods for “big data” analytics, sampling theory, statistical signal processing, machine learning, wireless communications.

Dr. Bajwa has more than three years of industry experience, including a summer position at GE Global Research, Niskayuna, NY. He received the Best in Academics Gold Medal and President’s Gold Medal in Electrical Engineering from the National University of Sciences and Technology in 2001, the Morgridge Distinguished Graduate Fellowship from the University of Wisconsin-Madison in 2003, the Army Research Office Young Investigator Award in 2014, the National Science Foundation CAREER Award in 2015, and Rutgers University’s Presidential Merit Award and Rutgers Engineering Governing Council ECE Professor of the Year Award in 2016. He is a co-author on the paper that received the Best Student Paper Award at the IEEE IVMSP 2016 Workshop, and he was selected as a Member of the Class of 2015 National Academy of Engineering Frontiers of Engineering Education Symposium. He co-guest edited a special issue of Elsevier Physical Communication Journal on “Compressive Sensing in Communications” (2012), co-chaired CPSWeek 2013 Workshop on Signal Processing Advances in Sensor Networks and IEEE GlobalSIP 2013 Symposium on New Sensing and Statistical Inference Methods, and served as the Publicity and Publications Chair of IEEE CAMSAP 2015. He is an Associate Editor of the IEEE Signal Processing Letters, a Senior Member of the IEEE, and serves on the MLSP, SAM, and SPCOM Technical Committees of the IEEE Signal Processing Society.

Tong Wu

Intended degree: PhD
Joining date: Fall 2012
Previous affiliations: Duke University (MS); Shanghai Jiao Tong University (BS)
Research interests: Data-driven geometric signal models; computer vision
Other information: Best Student Paper Award, IVMSP 2016; ECE Research Excellence Award, 2015; ECE Student Development Award, 2013; Visiting Student Researcher, Army Research Lab; Summer Intern, AT&T Research Lab

Haroon Raja

Intended degree: PhD
Joining date: Spring 2013
Previous affiliations: National University of Sciences and Technology (BS/MS)
Research interests: Distributed information processing; distributed optimization
Other information: Summer Intern, Bell Labs

Talal Ahmed

Intended degree: MS/PhD
Joining date: Spring 2013
Previous affiliations: Lahore University of Management and Sciences (BS)
Research interests: High-dimensional statistics; machine learning
Other information: ECE Research Excellence Award, 2013; IEEE SPS Student Travel Award, ICASSP 2013; Summer Intern, AT&T Research Lab

Zahra Shakeri

Intended degree: MS/PhD
Joining date: Fall 2013
Previous affiliations: Sharif University of Technology (BS)
Research interests: Information processing for tensor data; dictionary learning
Other information: IEEE Student Travel Awards, ISIT 2016; ECE Research Excellence Awards, 2015; ECE Best TA Award, 2015; Summer Intern, Technicolor Labs

Muhammad Asad Lodhi

Intended degree: PhD
Joining date: Fall 2014
Previous affiliations: Lahore University of Management and Sciences (BS/MS)
Research interests: Subspace-based information processing; computational imaging
Other information: ECE Best TA Award, 2015

Zhixiong Yang

Intended degree: PhD
Joining date: Spring 2015
Previous affiliations: Northeastern University (MS); Beijing Jiaotong University (BS)
Research interests: Distributed information processing; distributed optimization
Other information: ECE Student Development Award, 2016

Marielle Jurist

Joining date: Fall 2016
Major: Mathematics and Computer Science, Rutgers University
Project: Distributed averaging consensus for the internet-of-things
Other information: Aresty RA, 2016 – 2017

Zachary Blanco

Joining date: Fall 2016
Major: Electrical and Computer Engineering, Rutgers University
Project: Distributed averaging consensus for the internet-of-things
Other information: Aresty RA, 2016 – 2017

Kien Nguyen

Joining date: Fall 2016
Major: Electrical and Computer Engineering, Rutgers University
Project: GPU implementation of compressive sensing reconstruction algorithms
Other information: N/A

Seerat Aziz

Joining date: Spring 2017
Major: Electrical and Computer Engineering, Rutgers University
Project: Variable screening for high-dimensional statistics
Other information: N/A

Andrew Harms (2011 – 2013)

Degree: PhD, Electrical Engineering, Princeton University (co-advised with R. Calderbank)
Thesis title: Considerations on the optimal and efficient processing of information-bearing signals
Last known affiliation: Assistant Professor, Department of Electrical and Computer Engineering, University of Nebraska-Lincoln
Other information: Postdoctoral researcher at Duke University, 2013 – 2016

Neha Tadimeti (2015 – 2016)

Degree: MS, Electrical Engineering, Rutgers University
Thesis title: Multilinear algebra based techniques for foreground and background separation
Last known affiliation: Deep Learning R&D Engineer, NVIDIA
Other information: N/A

Xinnan Cao (2014 – 2015)

Degree: MS, Electrical Engineering, Rutgers University (co-advised with M. Javanmard)
Thesis title: Detrending and denoising of impedance cytometry data
Last known affiliation: Engineer, LT Security Inc.
Other information: N/A