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 Political Science 3768: Experimental Research, Fall 2021. Instructor: Yamil Velez.
Teaching Assistant for Political Science 3720: Research Design (Scope and Methods), Summer 2020 and Summer 2021. Instructor: Michael Miller.
Teaching Assistant for Political Science 4461: Latin American Politics, Fall 2020. Instructor: Vicky Murillo.
Teaching Assistant for Political Science 3565: Drugs and Politics in the Americas, Spring 2020. Instructor: Eduardo Moncada.
Teaching Assistant for Political Science 1501: Introduction to Comparative Politics, Fall 2019. Instructor: Kimuli Kasara.
Short Courses / Workshops
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.
Introduction to R presented for incoming Columbia Political Science PhD students, on August 27th, 2019. I made the presentation with learnr, so it also functions as an online tutorial, which you can find here.