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University of Massachusetts


Modeling Social Data

Abstract: This talk provides an overview of several recent projects in modeling social data that incorporate various aspects of applied statistical inference and machine learning. First, we present a Bayesian approach to inferring community structure in large-scale networks that gives rise to a scalable and efficient variational algorithm for fitting and comparing network models. Next, we discuss work with the Yahoo! Mail team which aims to infer associations and groups amongst and individual's contacts. Subsequently, we present an interpretable but effective temporal model of communication patterns which, phrased as a hidden Markov model, provides an effective and interpretable characterization of both human and non-human activity. We conclude with a study which pairs browsing histories for 250,000 anonymized individuals with user-level demographic data to study variation in Web activity among different demographic groups

Bio: Jake Hofman is a research scientist in the Human Social Dynamics group at Yahoo! Research in New York. His work involves data-driven modeling, focusing on applications of machine learning and statistical inference to large-scale social data. He holds a Ph.D. in Physics from Columbia University and a B.S. in Electrical Engineering from Boston University.

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