Teaching
I teach quantitative methods, causal inference, machine learning, and American politics. I am especially interested in helping students connect modern statistical tools to real research questions in political science and the social sciences.
Featured Materials
Harvard Political Methodology
Courses in data science, data analysis, and applied machine learning for political science and the social sciences.
Causal Machine Learning Course
Topical workshop syllabus for ICPSR’s Causal Machine Learning for Observational and Experimental Research.
Causal Machine Learning Lectures
Slides from invited and summer-program lectures on causal forests, doubly robust machine learning, and applied political science.
Introduction to R
Introductory R lab materials used at the ICPSR Summer Program and with incoming students at UC Davis.
Machine Learning Labs
Applied lab materials for unsupervised learning and interpretable machine learning.
Instructor Positions
Harvard University, Lecturer in Political Methodology: GOV 50 Data Science for the Social Sciences; GOV 51 Data Analysis and Politics; GOV 2018 Applied Machine Learning (PhD class) (2026)
ICPSR Summer Program, Topical Workshop: Causal Machine Learning for Observational and Experimental Research (2025-26, with Jack T. Rametta)
Teaching Assistantships
Graduate Courses
ICPSR Summer Program: Machine Learning, Applications in Social Science Research (Summer 2019-2026, Christopher D. Hare)
ICPSR Summer Program: Machine Learning, Applications in Social Science Research, one-week workshop (Summer 2020)
Undergraduate Courses
Harvard
GOV 1314: Race in American Society (Spring 2026, Marcel Roman)
GOV 1372: Political Psychology (Fall 2025, Ryan D. Enos)
UC Davis
POL 051: The Scientific Study of Politics (Research Methods) (Spring 2020, Christopher D. Hare)
POL 147B: The Legislative Process (U.S. Congress) (Spring 2018, Erik Engstrom)
POL 012A: Elections & Voting Behavior (Winter 2018, Christopher D. Hare)
Lectures, Labs, & Workshops
Northwestern Causal Machine Learning Lecture: Presented in POL 490, Machine Learning in Political Science.
Binghamton Causal Machine Learning Lecture: Presented to the Political Science Research Workshop.
2024 ICPSR Blalock Lecture on Causal Machine Learning: Taught at the ICPSR Summer Program.
Introduction to R: Taught at the ICPSR Summer Program and to incoming students at UC Davis.
Introduction to ML in Political Science: Taught at the Inaugural Methods Lunch Talk Series at UC Davis.
PCA & cPCA Lab (For ICPSR): Taught as part of a lecture on unsupervised machine learning.
Interpretable Machine Learning Lab (For ICPSR): Taught as an additional lab.