On extremal auxiliaries in multiuser information theory

There has been a collection of recent results, some establishing capacity regions in special classes of channels, while others evaluating inner and outer bounds for capacity regions and showing that they differ. Such results originate from establishing some new techniques for evaluation of various regions in multiuser information theory, and in particular, results that help one narrow (or identify) the extremal auxiliary random variables. This talk will give an overview of the techniques that have proved instrumental in various evaluations of regions.

This talk will not be about a single piece of work but rather a collection of collaborative results over the years.
Prof. Nair is an Associate professor with the department of Information Engineering at The
Chinese University of Hong Kong. He received a Masters (2002) and PhD (2005) in electrical
engineering from Stanford University. He was a Stanford Graduate Fellow (2000-2004) and then a Microsoft Graduate Fellow (2004-2005) during his graduate studies. Then he became a postdoctoral fellow at the (math) theory group in Microsoft Research for two years.

His current interests are in basic network information theory problems. He has previously
worked on problems touching many areas including combinatorial optimization, statistical physics, algorithms, and networks. He is currently serving as an associate editor for the IEEE Transactions on Information Theory.