Teaching

Cornell

My primary teaching interests are in quantitative methods/data science.

In fall 2026, I will be teaching a graduate-level course, Analysis of Natural Experiments (GOVT 6129/4000), which covers recent advances in observational causal inference. Major themes include what designing for inference means in observational settings, double robustness/double machine learning, and leveraging case knowledge to bolster causal assumptions.

In spring 2027, I will be teaching in the Government Department’s graduate quantitative methods sequence (GOVT 6029).

Previously, I have taught these courses as well as an undergraduate-level course on political behavior (GOVT 1101).

Students who are interested in my classes or who want to chat about research should feel free to email me!

Yale

While a graduate student at Yale, I was a Teaching Fellow for classes on causal inference, data exploration and analysis, and American politics. I have uploaded many of my teaching files from those courses to my Github.

Also while at Yale, I received a Certificate of College Teaching Preparation from the Yale Poorvu Center for Teaching and Learning.