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The Dynamics of Active Sensing in Social Networks
Date: 2014/12/16             Browse: 472

Speaker: Vikram Krishnamurthy

Time: Dec. 16, 4:00-5:00 pm

Location: Room 220, Building 8, Yueyang Road Campus

Abstract:

Large scale social networks present unique challenges from a statistical signal processing point of view since agents interact with and influence other agents. Also agents reveal decisions and not private observations. There is strong motivation to construct models that capture the interacting dynamics of multiple agents in social networks, together with algorithms that can be used to estimate events of interest. This lecture is comprised of two parts:

The first part considers Bayesian social learning models for the interaction of agents. Extensions to Bayesian data incest management in online reputation systems will be described.

The second part of the talk deals with models for the propagation of information in large scale social networks modelled as random graphs. Examples include the spread of information on social media, localization and tracking using social networks. The aim is to give the audience an understanding of recent results in the  signal processing of social networks and multi-agent systems.   

Bio:

Vikram Krishnamurthy holds the Canada Research Chair in signal processing at the Department of Electrical and Computer Engineering, University of British Columbia, Canada. His current research interests include social networks, computational game theory and stochastic control. He has served as Distinguished lecturer for the IEEE signal processing society and as Editor in Chief of IEEE Journal Selected Topics in Signal Processing. In 2013 he was awarded an honorary doctorate from KTH (Royal Institute of Technology), Sweden.                                                                                                                                                                               SIST-Seminar 14044