I study political and social behavior, especially relationships between individuals and institutions in democratic societies. My research examines how people perceive and respond to the influence of governments and other institutions and how these dynamics shape electoral behavior and support for policies addressing critical societal challenges, including inequality and climate change.

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.

My work has been published in PNAS, the Journal of Environmental Psychology, Humanities and Social Sciences Communications, and the Journal of Public Health Policy.

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.