Deniz Gunduz received the B.S. degree in electrical and electronics engineering from METU, Ankara, Turkey, in 2002, and the M.S. and Ph.D. degrees in electrical engineering from New York University Polytechnic School of Engineering , Brooklyn, NY in 2004 and 2007, respectively. He is currently a Professor of Information Processing in the Electrical and Electronic Engineering Department of Imperial College London. Previously he was a Research Associate at CTTC , Barcelona, Spain, a Consulting Assistant Professor at the Department of Electrical Engineering, Stanford University , and a postdoctoral Research Associate at the Department of Electrical Engineering, Princeton University. He is also a part-time faculty member at the University of Modena and Reggio Emilia, Italy, and has held visiting positions at University of Padova (2018-2020) and Princeton University (2009-2012).
Dr. Gündüz is a Fellow of the IEEE, and a Distinguished Lecturer for the IEEE Information Theory Society (2020-22). He serves as an Area Editor for the IEEE Transactions on Information Theory, IEEE Transactions on Communications, and the IEEE Journal on Selected Areas in Communications (JSAC) - Special Series on Machine Learning in Communications and Networks. Dr. Gunduz is also an editor of the IEEE Transactions on Wireless Communications. He served as a co-chair of the IEEE Information Theory Society Student Committee from 2012 until 2015. He served as the general co-chair of the 2016 IEEE Information Theory Workshop, and was a co-chair of the 2012 European School of Information Theory (ESIT).
He is the recipient of the IEEE Communications Society - Communication Theory Technical Committee (CTTC) Early Achievement Award in 2017, Starting Grant of the European Research Council (ERC) , the 2014 IEEE Communications Society Best Young Researcher Award for the Europe, Middle East, and Africa Region, and several best paper awards including the 2007 IEEE International Symposium on Information Theory (ISIT) Best Student Paper Award.
His research interests lie in the areas of communications and information theory, machine learning, and privacy.
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