[talks] TODAY 10/1 at 4:30pm in B205 - Chandra Nair, Chinese University of Hong Kong

[Emily Lawrence]

EE SEMINAR SERIES

 

 


 


Speaker: 

Chandra Nair, Chinese University of Hong Kong


Title: 

Non-Convex Optimization And Multiuser Information Theory


Day: 

Monday, October 1, 2018


Time:

4:30 pm


Room: 

B205 Engineering Quadrangle


Host:

Prof. Peter Ramadge

 

 

 

 

 

 

 

 

 

 

 

Abstract:

Capacity regions in multiuser information theory were traditionally
established using an achievability argument and converses to corresponding
rate regions. Recently, however, capacity regions have been established, for
non-trivial and important classes of channels (such as additive Gaussian
noise models and others), by optimizing functionals on probability spaces
generated from inner or outer bounds and showing that the bounds match for
these channels. Optimization ideas have also been used to determine if
certain achievable regions proposed in the literature are optimal or
sub-optimal in general. The study of these non-convex optimization problems
required developing new insights and techniques. In this talk, I will
outline the ideas involved in obtaining the results, as well as illustrate
the relationship of these problems to hypercontractivity and related notions
studied primarily outside information theory. I will present some unifying
observations across the family of optimization problems that we have managed
to solve, and state some elementary instances of similar problems whose
solutions would be of immense interest.

 

Bio:
Chandra Nair is an Associate Professor with the Information Engineering
department at The Chinese University of Hong Kong. His research interests
and contributions have been in developing ideas, tools, and techniques to
tackle families of combinatorial and non-convex optimization problems
arising primarily in the information sciences. His recent research focus has
been on studying the optimality of certain inner and outer bounds to
capacity regions for fundamental problems in multiuser information theory.
He, along with his doctoral student Yanlin Geng, received the 2016
Information Theory Society paper award for developing a novel way to
establish the optimality of Gaussian distributions. A proof of the Parisi
and Coppersmith-Sorkin conjectures in the Random Assignment Problem
constituted his doctoral dissertation; and he resolved some conjectures
related to Random Energy model approximation of the Number Partition Problem
during his post-doctoral years.

Chandra Nair got his Bachelor's degree, B.Tech(EE), from IIT Madras (India)
where he was the Philips (India) and Siemens (India) award winner for the
best academic performance. Subsequently he was a Stanford graduate fellow
(00-04) and a Microsoft graduate fellow (04-05) during his graduate studies
at the EE department of Stanford university. Later, he became a
post-doctoral researcher (05-07) with the theory group at Microsoft
Research, Redmond. He has been a faculty member of the information
engineering department at The Chinese university of Hong Kong since Fall
2007. He was an associate editor for the IEEE Transactions on Information
Theory (2014-2016) and is currently a distinguished lecturer of the IEEE
Information theory society. He is a fellow of the IEEE. He serves as the
Programme Director of the undergraduate program on Mathematics and
Information Engineering and the Director of the Institute of Theoretical
Computer Science and Communications.

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