Vikram Krishnamurthy

  Humans can be viewed as social sensors that input information to a social network. The interaction of social sensors present several challenges from a statistical signal processing viewpoint: sensors interact with and influence other social sensors resulting in herding behaviour. Second, due to privacy concerns, social sensors  reveal quantized decisions (ratings, recommendations).  Third, social sensors are risk averse decision makers with anticipatory emotions. This talk describes mathematical models for how social sensors interact over a social network, how social sensor decision-making can result in herding behaviour, and how herding can be mitigated by providing incentives to individual sensors. We will also discuss novel methods to poll social networks based on expectation polling and the friendship paradox. The seminar draws from ideas in statistical signal processing and behavioral economics.

From 2002-2016 he was the Canada Research Chair professor in statistical signal processing at the University of British Columbia, Canada. His current research interests include statistical signal processing and stochastic control with applications in social networks. Dr Krishnamurthy has served as Distinguished lecture for the IEEE signal processing society and Editor in Chief of IEEE Journal Selected Topics in Signal Processing. He received a honorary doctorate from KTH (Royal Institute of Technology), Sweden in 2013.