Title: Concentration inequalities in systems and control
Abstract: Concentration inequalities, which provide exponential upper bounds on the probability that a given function of many independent (or weakly dependent) random variables deviates significantly from its mean or median, have been the subject of exciting developments during the last two decades. They appear prominently in such fields as convex geometry, functional analysis, statistical physics, mathematical statistics, pure and applied probability theory, information theory, theoretical computer science, learning theory, and dynamical systems. This talk, which will be mostly of a tutorial nature, will focus on the basic theory underlying concentration inequalities, as well as on some of their applications to problems in systems and control.
Biography: Maxim Raginsky received the B.S. and M.S. degrees in 2000 and the Ph.D. degree in 2002 from Northwestern University, all in electrical engineering. He has held research positions with Northwestern, the University of Illinois at Urbana- Champaign (where he was a Beckman Foundation Fellow from 2004 to 2007), and Duke University. In 2012, he has returned to UIUC, where he is currently an Assistant Professor with the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory. In 2013, Prof. Raginsky has received a Faculty Early Career Development (CAREER) Award from the National Science Foundation. Prof. Raginsky is a Senior Member of IEEE, and serves on the editorial board of Foundations and Trends in Communications and Information Theory. His research interests lie at the intersection of information theory, machine learning,