Tsachy Weissman is Professor of Electrical Engineering at Stanford University since 2003. His research focuses on Information Theory and Communications and Statistical Signal Processing. His is the recipient of NSF CAREER award and several best paper awards including the Horev fellowship for Leaders in Science and Technology, Henry Taub prize for excellence in research, incumbent of the STMicroelectronics Chair in the School of Engineering, and IEEE fellow. Weissman is serving on the editorial boards of the IEEE Transactions on Information Theory and Foundations and Trends in Communications and Information Theory. He is also the Founding Director of the Stanford Compression Forum.
In this lecture, Weissman will discuss information theory and statistical signal processing, the interplay between them, and their applications. He will survey some of the activity in his group pertaining to inference and data compression, including: Justification of inference under logarithmic loss; Estimation of the associated information measures from 'big data', and its applications; Compression under logarithmic loss; and Successively refinable lossy compression, with applications to genomic data.