Short Bio

Currently, I am a Ph.D student in the department of computer science at Brown University working with Erik Sudderth. My interests lie in the development of probabilistic graphical models to help discover hidden structures within complex datasets such as documents, images, biological data, and more. The unique problems associated with each dataset motivates the need to develop new and interesting models that can best represent these latent structures.

 

I'm also quite interested in spreading the beauty and usefulness of these tools to the wider public. I've been working on developing intuitive visualizations that anyone can use with a little dedication to help better express the results that come from applying these models to interesting datasets. More specifically, much of my work has been with large document corpuses and examples of visualizing these results can be seen here.

News

9/1/2011: The Doubly Correlated Nonparametric Topic Model has been accepted into NIPS 2011! The paper is an exciting extension to current topic models that allows for correlated topics, document metadata, and a nonparametric prior which allows for a potentially infinite number of topics.

Current Work

My current active research area is to develop unsupervised graphical models for relational datasets such as social networks, email communications, or even biological networks such as the neural network of C.elegans.