Cassandra Handan-Nader is an Assistant Professor of Political Science at New York University. Her research develops and applies machine learning techniques to measure and characterize the structure of conflict in American political institutions. She’s 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 for large-scale regulatory and legal applications.

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