I’m a computational social scientist/data scientist and am currently pursuing a PhD at the University of Pennsylvania. I study the causes and consequences of partisanship, with a specific focus on affective polarization, partisan identity, and political violence. My research methodology involves harnessing data from diverse sources such as observational and trace data, surveys, and experiments, and utilizing a wide range of statistical techniques. I leverage both classical statistical methods such as regression, decision trees, PCA, and bayes factors, as well as cutting-edge methods such as machine learning, natural language processing, XGBoost, and neural networks to analyze and interpret complex data sets.


University of Pennsylvania
PhD Communication | 2021 - Present

The Wharton School at University of Pennsylvania
MA Statistics and Data Science | 2023

University of Amsterdam
MSc Political Communication | 2021

UC Santa Barbara
BA Political Science and Psychology | 2014


The Wharton School
Head Statistics and Data Science TA | 2022 - Present

University of Pennsylvania
Computational Research Fellow | 2022 - Present