Princeton University

School of Engineering & Applied Science

Data Processing Theorems and Applications

Dr. Sudeep Kamath, University of California, San Diego
E-Quad B205
Tuesday, March 25, 2014 - 4:30pm

Abstract:  Understanding the amount of information loss in a randomized map is an important problem in a variety of contexts, such as stochastic simulation, learning, and computation in noisy circuits. Data processing theorems attempt to characterize this loss of information.

In this talk, we will consider so-called strong data processing inequalities. Simply stated, a data processing inequality guarantees that processing of data may only reduce its information content while a strong data processing inequality makes this precise by quantifying the loss. We will consider the question of the tightest possible strong data processing inequalities for mutual information and discuss some implications for results in the literature on multi-terminal information theory.
(Joint work with Venkat Anantharam, Amin Gohari, and Chandra Nair.)
Bio: Sudeep Kamath received the B.Tech degree in Electrical Engineering from the Indian Institute of Technology Bombay in 2008 and Ph.D. from the EECS department, University of California, Berkeley in 2013. His research interests lie broadly in information theory. He is a recipient of the Eliahu Jury Award from the EECS department of UC Berkeley (2013). He is currently a postdoctoral researcher at UC San Diego.
Host:  Professor Sergio Verdú