I have a broad interest in understanding the ways that institutional rules and market forces combine to shape the behaviors and attitudes of party elites, candidates, and voters. My work relies on big administrative data with millions of observations, geographic information systems (GIS), and programming with MATLAB, Python, and R. I currently study political behavior surrounding major climate events in the United States, and I adapt geostatistics for social science applications. I have prior experience applying Bayesian item response theory, causal inference, natural language processing, and structural models of discrete choice to answer questions in political science.
Listed skills include Economic Modeling, Statistics, Numerical Linear Algebra, Optimization, and 8 others.