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. PUBLICATIONS  Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process [PDF] [Supplement] [Code] in AISTATS 2015 We develop a new objective function for the HDP topic model that allows merge and delete moves to remove ineffective topics during training.  in NIPS 2013 We develop new learning algorithms for scalable variational inference, including new birth and merge moves. Start with just one cluster, and grow as needed.   in NIPS 2012
We develop new data-driven inference methods that enable unsupervised behavior discovery in hundreds of motion capture sequences.
Nonparametric Metadata-Dependent Relational Model [PDF] in ICML 2012
We find community structure in social & ecological networks, using metadata like age or organism type to improve community discovery.
 in POCV 2012 (a workshop at CVPR)
Across many videos, we identify short segments showing the same human activity (motion and appearance), without predefining relevant activities or even their number.
TECH REPORTS Sampling From Truncated Normal
|
 | |
|
|
