My research interests lie in computational social science, at the intersection of political methodology and comparative politics. Broadly, my research agenda seeks to innovate and refine methodological techniques that bridge qualitative and quantitative data sources, particularly for applications to the study of authoritarian regimes. My dissertation adapted methods from machine learning to demonstrate how an elicited-priors approach can improve statistical inference, particularly in authoritarian or developing contexts, by incorporating diverse sources of information. My current research agenda builds on this work and comprises three sets of projects: uncertainty estimation in text-as-data; prior elicitation for Bayesian analyses; and methodologically rigorous applied work investigating authoritarian institutions.
Below are several of my current working papers and publication titles--copies available upon request.