PhD Candidate, Rice University
Deep Learning Approaches to Structured Signal Recovery
The promise of compressive sensing (CS) has been offset by two significant challenges. First, real-world data is not exactly sparse in a fixed basis. Second, current high-performance recovery algorithms are slow to converge, which limits CS to either non-real-time applications or scenarios where massive back-end computing is available. We attack both of these challenges head-on by developing new signal recovery frameworks using deep learning techniques.
Ali Mousavi is currently pursuing the Ph.D. degree in the Department of Electrical and Computer Engineering at Rice University under supervision of Richard Baraniuk. He received B.Sc. degrees in electrical engineering from Sharif University of Technology in 2011. He received M.Sc. degree in electrical and computer engineering from Rice University in 2014. During summer 2010, he was a visiting researcher in laboratory of information and communication systems in École Polytechnique Fédérale de Lausanne under supervision of Suhas Diggavi. His research interests include machine learning and statistical signal processing, with a current emphasis on applications of data science in signal processing. Mr. Mousavi has been awarded Rice University George R. Brown School of Engineering Fellowship, Ken Kennedy Institute Schlumberger Graduate Fellowship, and Society of Iranian-American Women for Education Fellowship.
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