I study how institutions shape individuals’ behaviors and beliefs in political contexts and critical societal challenges such as climate change and inequality—and how individuals, in turn, shape institutions. I am primarily interested in understanding how trust—both interpersonal and institutional—and perceptions of fairness influence policy support.

My methodological approach integrates computational text analysis, advanced statistical models, economic games, survey experiments, and formal and agent-based modeling to model and examine behaviors, policy preferences, beliefs, norms, emotions, and narratives. By analyzing large-scale unstructured textual datasets, survey responses, and behavioral choices from social media, rally speech, survey experiments, and economic games, I model and 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.