Abstract: Compressive sensing (CS) promises to reduce the sampling burden for most signals of interest by exploiting their structure and the wide availability of inexpensive computation. While theoretical developments are promising, exploiting CS in practice poses significant challenges. In this talk will briefly present the basic compressive sensing theory and how it can be very useful in practical systems. We will examine several applications―such as ultrasonic sensing, microphone and radar array processing, and depth sensing―and discuss how CS ideas, when properly exploited, can significantly advance the capabilities of those systems.
Biography: Petros T. Boufounos is a Principal Member of Research Staff at Mitsubishi Electric Research Laboratories in Cambridge, MA and a visiting scholar at the Rice University Electrical and Computer Engineering department in Houston, TX. Dr. Boufounos completed his undergraduate and graduate studies at MIT. He received the S.B. degree in Economics in 2000, the S.B. and M.Eng. degrees in Electrical Engineering and Computer Science (EECS) in 2002, and the Sc.D. degree in EECS in 2006. Between September 2006 and December 2008, he was a postdoctoral associate with the Digital Signal Processing Group at Rice University. Dr. Boufounos joined MERL in January 2009.
Dr. Boufounos immediate research interests include signal acquisition and processing, quantization and data representations, frame theory, and machine learning applied to signal processing. He is also looking into how signal acquisition interacts with other fields that use sensing extensively, such as robotics and mechatronics. Dr. Boufounos is an associate editor at IEEE Signal Processing Letters. He has received the Ernst A. Guillemin Master Thesis Award for his work on DNA sequencing, the Harold E. Hazen Award for Teaching Excellence, both from the MIT EECS department, and has been an MIT Presidential Fellow. He is also a member of the IEEE, Sigma Xi, Eta Kappa Nu, and Phi Beta Kappa.