My research focuses on political and social behavior. Specifically, I study how institutions shape individuals’ behaviors and beliefs in politics and in response to critical societal challenges such as climate change and inequality—and how individuals, in turn, shape institutions.

My research is both theory- and data-driven. I use a diverse methodological approach that integrates natural language processing, advanced statistical models, economic games, survey experiments, and formal and agent-based modeling to study behaviors, policy preferences, beliefs, norms, emotions, and narratives. I develop formal models of behavior and simulate them using agent-based modeling. I also draw on a variety of data sources. By analyzing large-scale unstructured text data, survey responses, and behavioral choices from platforms such as social media, rally speeches, survey experiments, and economic games, I investigate the underlying mechanisms that drive behavior and policy support. You can find my publications and projects using these methodologies in the Research tab.

I teach Computational Text Analysis for Social Sciences, which covers machine learning and natural language processing using both R and Python. You can find the course materials on my teaching page.

I am a postdoctoral researcher at the University of Pennsylvania in Philosophy, Politics, and Economics and the Center for Social Norms and Behavioral Dynamics . I received my Ph.D. from the department of Political Science at Stony Brook University in 2023.