Consensus in context : leveraging the network to accelerate distributed consensus

Seminar
Friday, October 30, 2009
11:00am
ENS 637

Gossip algorithms are a class of decentralized solutions to theproblem of achieving consensus in a network of agents. Theyhave attracted recent research interest because they are simpleand robust -- attractive qualities for wireless ad-hoc and sensornetworks. Unfortunately, the standard gossip protocol convergesvery slowly for many popular network models. I will discuss threeways to leverage properties of the network to achieve fasterconvergence : routing, broadcast, and mobility.

Joint work with Alex G. Dimakis, Tuncer Can Aysal, Mehmet ErcanYildiz, Martin Wainwright, and Anna Scaglione.

Bio:Anand Sarwate is currently a Postdoctoral researcher at the Information Theory and Applications Center at the University of California, San Diego. He earned BS degrees in Electrical Engineering and Mathematics from MIT in 2002 and MS and PhD degrees in Electrical Engineering from the University of California, Berkeley in 2005 and 2008, where he was under the supervision of Professor Michael Gastpar. Dr. Sarwate received the Samuel Silver Memorial Scholarship Award and Demetri Angelakos Memorial Achievement Award from the EECS Department at UC Berkeley. His research interests include information theory, distributed signal processing, machine learning, communications, and randomized algorithms for communications and signal processing in sensor networks.

Speaker

Postdoctoral Researcher
UCSD

Bio:Anand Sarwate is currently a Postdoctoral researcher at the Information Theory and Applications Center at the University of California, San Diego. He earned BS degrees in Electrical Engineering and Mathematics from MIT in 2002 and MS and PhD degrees in Electrical Engineering from the University of California, Berkeley in 2005 and 2008, where he was under the supervision of Professor Michael Gastpar. Dr. Sarwate received the Samuel Silver Memorial Scholarship Award and Demetri Angelakos Memorial Achievement Award from the EECS Department at UC Berkeley. His research interests include information theory, distributed signal processing, machine learning, communications, and randomized algorithms for communications and signal processing in sensor networks.