Cassandra Handan-Nader

Cassandra Handan-Nader is a Ph.D. candidate in Political Science with a Ph.D. minor in Computational and Mathematical Engineering. Her research develops and applies machine learning techniques to measure and characterize the structure of conflict in American political institutions. She is 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 applying machine learning methods to address large-scale regulatory problems.