My research investigates the factors that motivate or demotivate individuals to engage in behaviors and support policies addressing critical societal challenges, such as climate change, the COVID-19 pandemic, and inequality. By integrating frameworks from behavioral political economy and political psychology, I aim to understand the underlying mechanisms driving these behaviors and policy preferences. Specifically, I am interested in how individuals’ trust in each other and institutions, perceptions of fairness, and views on limiting individual choices for collective goods influence their support for crisis mitigation policies.

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, norms, emotions, and narratives. By analyzing large-scale unstructured datasets, survey responses, and behavioral choices from social media, survey experiments, and economic games, I aim to uncover the underlying mechanisms that drive behavior and policy support.

I am teaching Computational Text Analysis for Social Sciences in Fall 2024, 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.