Anand Sarwate of Rutgers University

The goal of this work is to revisit the gap in information-theoretic modeling of interference in communication systems. Shannon's original work used an average-case (random noise) model, whereas coding theory has focused on worst-case models. For binary codes, this corresponds to a gap between assuming iid Bernoulli errors or adversarial errors. We look at models that are in between these two extremes. A causal adversary has to decide whether to cause an error based only on the transmitted codeword so far. A myopic adversary may have only noisy access to the transmitted codeword. This talk will survey the landscape of these models and highlight some key structures for codes in this more general setting: stochastic encoding and list decoding.

This talk will discuss work with Amitalok Budkuley, Bikash Kumar Dey, Michael Gastpar, Sidharth Jaggi, Michael Langberg, Carol Wang, and Yihan Zhang, as described in a recent monograph<http://dx.doi.org/10.1561/0100000112<https://url.au.m.mimecastprotect.com/s/Ce1qCE8knvsl7ZgEOIZt2i7gwRC?domain=dx.doi.org>>.

Biography:

Anand D. Sarwate is a professor in the Electrical and Computer Engineering Department at Rutgers, The State University of New Jersey. He received B.S. degrees in mathematics and electrical engineering from MIT and a Ph.D. in electrical engineering from UC Berkeley. Prior to joining Rutgers he was a Research Assistant Professor at TTI-Chicago and a postdoc at the ITA Center at UC San Diego. His research interests include information theory, machine learning, signal processing, optimization, and privacy and security. Dr. Sarwate serves on the Board of Governors of the IEEE Information Theory Society (ITSOC) and is a ITSOC Distinguished Lecturer for 2024-2025.

Address:New Jersey, United States