معرفی
Marco Morucci is an Assistant Professor in the Department of Political Science at Michigan State University (MSU). His research focuses on developing empirical methods for political science in data-scarce environments, combining machine learning and causal inference. Applied work examines mass public action and corruption in developing countries.
Education: Ph.D. in Political Methodology from Duke University, where he collaborated with David Siegel, Cynthia Rudin’s Prediction Analysis Lab, the Almost Matching Exactly Lab, and Devlab@Duke. Previously, he was a faculty fellow at the Center for Data Science at New York University.
Research Interests: Innovating statistical methods for causal inference challenges, especially in low-data contexts. Applied projects include analyzing protest dynamics, constitutional structures via NLP, and corruption mechanisms in global contexts. His work bridges computational techniques with substantive political questions.
Contributions: GitHub repositories reflect his methodological and applied work, including robust causal inference frameworks, constitutional clustering analyses, and legislative behavior prediction. Active in open-source tools for political science.
Labs/Collaborations: Past affiliations include Duke’s interdisciplinary labs and NYU’s data science center. Current collaborations likely ongoing at MSU.
