Food and discussion begins at 12:30 pm. Everyone invited.

The past several decades have seen an explosion of digitized information in fields such as political science. In this period, a variety of statistical tools have been developed to better understand patterns in these records by incorporating information such as records of behavior and the text of documents. In this talk, I will present several recent statistical models for discovering patterns in digital records in the fields of politics, bibliometrics, and international relations. These models, which emphasize topic modeling, will link legislative text to voting patterns, written judges’ opinions to citations of these opinions, and the text of newspaper articles to countries’ international relations. I will also describe details of approximate posterior inference for these models, and will briefly touch upon advances in such methods. This talk includes ongoing work.

Bio:

Sean Gerrish is currently a PhD candidate in the Princeton Computer Science Department, where he studies applications of machine learning and statistical models to text datasets. Previously, Sean received a BS in mathematics at the University of Michigan and spent several years as a software engineer at Google, where he worked in their decision support and ads quality groups.