Princeton Neuroscience Institute

Curriculum Vitae

Publications

My research interests include:

• Probabilistic graphical models and approximate posterior inference
• Topic models, information retrieval, and text processing
• Nonparametric Bayesian statistics

Topic Modeling Code and Browsers

Much of my research is in topic modeling, building hierarchical probabilistic models of documents and other media to uncover latent structure in their contents. As an example of this research, here are slides from a recent talk on dynamic and correlated topic models applied to the journal Science . (Here is a video of the talk.)

The structure uncovered by topic models can be used to explore the otherwise unorganized collection: dividing documents according to their topics and using the hidden structure to determine similarity between documents. The following are browsers of large collections of documents built with topic models:

• A 50-topic browser of the 2006 arXiv.
• A 20-topic browser of The American Political Science Review
• A 100-topic browser of Science (1980-2000)

• Latent Dirichlet allocation (LDA) in C
• Correlated topic model (CTM) in C

Questions, comments, and suggestions about this code should be posted to the topic models mailing list.