Rocío Titiunik is a Professor of Politics at Princeton University and Director of the Data-Driven Social Science Initiative. She holds affiliations with the School of Public and International Affairs, the Department of Operations Research and Financial Engineering, the Center for Statistics and Machine Learning, the Program in Latin American Studies, the Center for the Study of Democratic Politics, and the Research Program in Political Economy. Her work bridges quantitative methodology, political economy, and statistical analysis, focusing on causal inference and program evaluation through regression discontinuity (RD) designs. She earned her undergraduate degree at the Universidad de Buenos Aires and a Ph.D. in Agricultural and Resource Economics from UC-Berkeley (2009). Before joining Princeton, she served as faculty at the University of Michigan’s Department of Political Science, where she was affiliated with the Center for Political Studies and the Michigan Institute for Data Science. Rocío’s research emphasizes the application of quasi-experimental methods to study political institutions, democratic accountability, and party systems in developing democracies. Her methodological contributions include advancements in RD designs, synthetic controls, and uncertainty quantification. Recent substantive work investigates charismatic leaders’ impact on democratic stability and the effects of voter registration reforms on public safety. Her scholarly achievements include the 2016 Emerging Scholar Award from the Society for Political Methodology and 2020 fellowship in the same society. She currently serves as an associate editor for Science Advances and a Board member for Science, while previously holding roles at the American Journal of Political Science and the NSF’s Social, Behavioral, and Economic Sciences Directorate. Rocío’s advising and grant activities include co-leading the EITM Summer Institute and securing federal research funding for projects on political methodology and democracy. She teaches advanced quantitative analysis courses and collaborates across disciplines to strengthen empirical research practices. Her work is anchored in collaborative initiatives like the Center for Statistics and Machine Learning, which integrates computational tools with social science inquiry, and the Program in Latin American Studies, reflecting her commitment to regional and methodological innovation.








