My PhD research
applies techniques from statistical machine learning (particularly
hierarchical Bayesian models and nonparametric extensions) to create
automated methods for extracting meaningful information from multimedia.
I study theoretical methods for efficient inference in graphical models
(e.g. MCMC sampling, variational methods) as well as practical ways to
apply these methods to real-world data such as video clips or text
documents. I'm part of the Learning, Inference, and Vision group here at Brown advised by Prof. Erik Sudderth. TECH REPORTS |
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Research |