Teaching

ALP 301: Data Driven Impact (graduate)

This course covers key considerations for designing and executing high-quality research for product innovation to drive business outcomes and social impact. Students have the opportunity to apply methods from machine learning and causal inference to a real-world scenario provided by a partner organization. Topics include designing research and experiments; data analysis; and experimental and non-experimental methods for estimating the impact of product features, as well as management consideration for the delivery of actionable research.

Graduate School of Business, Stanford University, TA for Prof. Susan Athey, Spring 2021

17.801 Political Science Scope and Methods (Undergraduate)

This course is designed to introduce undergraduates to a variety of empirical research methods used by political scientists, making them more sophisticated consumers of empirical research and to prepare them for their independent thesis work. Students work with TAs to collect data online, develop interview guides and conduct mock interviews, among other social science research techniques.

MIT Teaching Assistant, Fall 2017

17.802 Quantitative Research Methods II: Causal Inference (Graduate)

This is the second course in the graduate quantitative methods sequence in the MIT political science department. This course covers a variety of research designs and statistical methods for causal inference, including experiments, matching, regression, panel methods, difference-in-differences, synthetic control methods, instrumental variable estimation, regression discontinuity designs, causal mediation analysis, nonparametric bounds, and sensitivity analysis. Using applications from political science, public policy, economics and sociology, we discuss the strengths and weaknesses of these methods.

MIT Teaching Assistant, Spring 2016