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Abstract
A fundamental part of understanding human behavior is understanding social interactions between people. We would like to be able to make better predictions about social behavior so that we can improve people’s social interactions or somehow make them more beneficial. This is very relevant in light of the fact that an increasing number of interactions are happening in online environments which we design, but is also useful for offline interactions such as structuring interactions in the work place, or even being able to advise people about their individual health based on who they’ve come into contact with.I will focus on two recent projects. In the first we use nonparametric Bayesian methods to predict group structure in social networks based on the social interactions of individuals over time, based on actual events (emails, conversations, etc.) instead of declared relationships (e.g. Facebook friends). The time series of events is modeled using Hawkes processes, while relational grouping is done via the Infinite Relational Model.In the second, we use Graph-Coupled Hidden Markov Models to predict the spread of infection in a college dormitory. This is done by looking at a social network of students living in the dorm, and leveraging mobile phone data which reports on students’ locations and daily health symptoms.
Biography
Katherine Heller received a B.S. in Computer Science and Applied Math and Statistics from the State University of New York at Stony Brook, followed by an M.S. in Computer Science from Columbia University. In 2008 she received her Ph.D. from the Gatsby Computational Neuroscience Unit at University College London in the UK, and went on to do postdoctoral research in the Engineering Department at the University of Cambridge, and the Brain and Cognitive Science department at MIT. In 2012 she joined the Department of Statistical Science and Center for Cognitive Neuroscience at Duke University. She is the recipient of an NSF graduate research fellowship, an EPSRC postdoctoral fellowship, and an NSF postdoctoral fellowship. Her current research interests include Bayesian statistics, machine learning, and computational cognitive science.