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Facets of Information Theory
If you have a question about this talk, please contact Louise Segar.
Information theory aims to establish fundamental limits on information processing and to develop optimal schemes that achieve those limits. This talk focuses on three canonical problems in network information flow, communication engineering, and data science, and presents the state-of-the-art solutions to these problems from basic principles of information theory. In each problem area, we discuss a general approach—or a paradigm—that is beyond these specific case studies and applicable to a broad range of problems. Shannon’s bandwagon still offers an exciting (and sound) ride.
Young-Han Kim received his B.S. degree in Electrical Engineering from Seoul National University in 1996 and his Ph.D. degree in Electrical Engineering (M.S. degrees in Statistics and in Electrical Engineering) from Stanford University in 2006. Since then he has been a faculty member in the Department of Electrical and Computer Engineering at the University of California, San Diego, where he is currently an Associate Professor.
Professor Kim is a recipient of the NSF CAREER Award (2008), the US-Israel BSF Bergmann Memorial Award (2009), the IEEE Information Theory Paper Award (2012), and the first IEEE James L. Massey Research and Teaching Award (2015). He is an IEEE Fellow. His research interests include information theory, communication engineering, and data science. He has coauthored “Network Information Theory” (Cambridge University Press, 2011), which has been used widely as a textbook on the topic.
This talk is part of the Signal Processing and Communications Lab Seminars series.
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