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 Distinguished 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 is Fellow of AAAS (2019) and IEEE (2008), 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 (2009-2011), IEEE Signal Processing Society Vice President-Conferences (2006-2008), and is currently 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), to be held in Kalamata Greece in June 2018. She is recipient of the 2005 IEEE Signal Processing Magazine Best Paper Award, and the 2012 IEEE Signal Processing Society Meritorious Service Award for "exemplary service in technical leadership capacities". She was IEEE Distinguished Lecturer for the Signal Processing Society for 2017-2018.
Dr. Petropulu's research interests span the area of statistical signal processing, wireless communications, signal processing in networking, physical layer security, and radar signal processing.
Her work on MIMO radars based on sparse sensing has generated a lot of interest. Reliable surveillance requires collection, communication and fusion of vast amounts of data from various antennas. A. Petropulu and co-authors have pioneered new radar systems that use advanced signal processing techniques, such as compressive sensing and matrix completion in order to achieve the high resolution of MIMO radars but with significantly fewer data samples, or significantly higher resolution with the same number of samples. Fewer samples translate into lower communication overhead, which is important savings when the radar antennas are on the nodes of a sensor network and transmit their received samples to the fusion center via a wireless link. In that context, she has developed novel theoretical results on target recoverability and performance guarantees. She has also contributed to the topic of spectrum sharing between radar and communication systems, and has studied radar privacy issues arising in spectrum sharing scenarios.