About me

I am an Assistant Professor of Politics at New York University. My research develops and applies machine learning techniques to measure and characterize the structure of conflict in American political institutions. I’m particularly interested in developing new methods to measure the extent and trajectory of partisan polarization in the U.S. Congress, leveraging large-scale datasets to estimate the ideological positions of candidates for state and federal office, and adapting machine learning methods to address large-scale regulatory problems and legal applications.

Before joining NYU, I completed a Ph.D. in Political Science and a Ph.D. minor in Computational and Mathematical Engineering at Stanford University. Prior to that, I was a research fellow at the Regulation, Evaluation, and Governance Lab at Stanford Law School.