Teaching Assistant for Statistics 5293: Advanced Statistical Modeling for Social Science, Spring 2023. Instructor: Andrew Gelman.
Covers Bayesian data analysis and Stan, multilevel regression, item-response and ideal point models, measurement error models, models for causal inference, and other advanced topics.
Teaching Assistant for Political Science 4726: Quantitative Methods IV - Topics in Political Methodology, Spring 2022. Instructor: Naoki Egami.
Covers machine learning (regularized regression, tree-based methods, double machine learning for causal inference), measurement methods (ideal point estimation, text analysis, unsupervised machine learning), external validity and meta-analysis, and causal inference with network and spatial data.
Teaching Assistant for Political Science 4722: Quantitative Methods II - Statistical Theory and Causal Inference, Spring 2021. Instructor: Naoki Egami.
Covers randomized experiments, estimation under ignorability, directed acyclic graphs, instrumental variables, regression discontinuity, difference-in-differences, and causal inference with panel data, as well as statistical theory essential for causal inference.
Teaching Assistant for introductory math camp for incoming Political Science PhD and masters students, August 2022 and 2023. Instructor: Benjamin Goodrich.
Introduction to R presented for incoming Columbia Political Science PhD and masters students, on August 27th, 2019. I made the presentation with learnr, so it also functions as an online tutorial, which you can find here.
Introduction to regular expressions in R presented at the Columbia Political Science Graduate Student Methods Workshop on March 1st, 2019. The repository can be found here, and you can find the presentation here.