Raviraj Adve received his BTech from the Indian Institute of Technology, Bombay, in 1990 and his PhD from Syracuse University in 1996, both in Electrical Engineering. His PhD thesis received the Syracuse University Outstanding Dissertation Award. He then joined Research Associates for Defense Conversion, Inc., working on contract with the Air Force Research Laboratory (AFRL), Rome, NY, USA. In August 2000 he joined the Dept of Electrical and Computer Engineering at the University of Toronto, where he currently holds the rank of Professor. Prof. Adve received the 2009 Fred Nathanson Young Radar Engineer of the Year award and was elevated to Fellow of the IEEE in 2017.

Prof. Adve is very active within the AES community, having served on the Radar Systems Panel, several sub-committees of the Panel, and as Associate Editor for the AES Transactions. In addition, he served – and then chaired – the AES Fellows Evaluation Committee, a critical activity within AES. He serves (and has previously chaired) the IEEE Dennis Picard Medal committee. In addition, he supports the AES radar related conferences as a regular member of the TPC and/or as an active reviewer.

Prof. Adve has broad research interests in radar signal processing, wireless communications, quantum radar and the effective use of machine learning techniques in solving hard problems. He was one of the first to analyze the impact of mutual coupling on space-time adaptive processing (STAP). His work with AFRL culminated in one of the first knowledge-based adaptive processing techniques, addressing detection in homogeneous and heterogeneous clutter scenarios.

Since joining the University of Toronto, Prof. Adve has continued his active participation in AES. He developed the “fast fully adaptive” STAP algorithm specifically for the suppression of ionospheric clutter. Expanding his research interests beyond airborne radar, he developed a propagation model for ionospheric and auroral clutter with applications in over-the-horizon and surface-wave radar, which was then used to develop effective signal processing techniques. More recently, Prof. Adve has focused on machine learning applications in cognitive radar, developing reinforcement learning (RL) approaches to effective task scheduling. Another recent thrust in Prof. Adve’s research is quantum sensing using coincidence detection amongst entangled photos and the use of Rydberg atoms to sense weak electromagnetic signals.