I am an assistant professor in the Department of Political Science at the University of Pittsburgh. My on-going research fits into two strands. First, I create new methods to facilitate political science research by leveraging the intersection of Bayesian methods and machine learning. My working papers (available here) create new methods to tackle a variety of common problems (heterogeneous effects, hierarchical models, ideal point estimation) where existing methods have limitations that constrain substantive researchers. Second, I focus on understanding legislative behaviour using text-as-data in a comparative context including studies on Europe, the United States, and Japan.
My research on these topics and others has been published or is forthcoming in journals including the American Political Science Review, American Journal of Political Science, Bayesian Analysis, Political Analysis, Comparative Political Studies, and Legislative Studies Quarterly.
Before coming to Pittsburgh, I received my PhD from the Department of Government at Harvard University in 2020 where I was an affiliate of the Institute for Quantitative Social Science and the Minda de Gunzberg Center for European Studies.
I can be contacted at mgoplerud[at]pitt.edu and a CV can be found here.