Teaching
I teach methods at the intersection of political science and statistics. My goal is to make the logic of causal inference and quantitative analysis transparent, building each result from explicit assumptions so students develop the habit of tracing every identification step back to what justifies it.
Instructor
Introduction to Causal Inference
University of Pittsburgh
An introduction to the potential outcomes framework and modern research designs for causal questions in political science. Topics include randomization, selection bias, the g-formula, difference-in-differences, synthetic controls, and instrumental variables, each developed from first principles with an emphasis on where identification assumptions succeed and fail.
Head Teaching Assistant
Research Methods in Political Science
University of Pittsburgh · Spring 2025
Introduction to the research design and quantitative methods sequence. Covered research design principles, hypothesis formulation, and foundations of statistical inference for political science applications.
Political Attitudes and Behavior
University of Pittsburgh · Fall 2024
Survey-based and experimental approaches to studying public opinion, ideology, and political participation. Led discussion sections focused on interpreting empirical evidence from observational and experimental research.
Introduction to Quantitative Political Analysis
University of Pittsburgh · Summer 2024
Undergraduate methods course covering probability, statistical inference, and regression with applications to political science data. Designed supplementary materials and held review sessions on R programming and data analysis.