DS100 | Causal Inference, University of Mannheim, Fall 2024 (with Prof. Marc Ratkovic).
Tutorials 1–2 were led by Prof. Ratkovic. My tutorials covered race and legislative responses; difference-in-differences; interpreting coefficients for categorical variables through a WTO application; power analysis for field experiments and sample-size determination; propensity-score, exact, and coarsened exact matching; decision trees and random forests; entropy balancing; causal forests; and regression discontinuity design. Decision trees were introduced in Tutorial 3 and revisited alongside random forests in Tutorial 9.
DS200 | Sampling and Data / Bayesian Statistics and LLMs, University of Mannheim, Fall 2024 (with Prof. Marc Ratkovic).
The first part of the course focused on Bayesian statistics and statistical computing, including beta regression, shrinkage and random effects, Gibbs sampling, Metropolis–Hastings, conjugate priors, Hamiltonian Monte Carlo, conjoint experiments with horseshoe priors and interaction effects, and multilevel regression and poststratification (MRP). The final tutorials transitioned to large language models and chatbot workflows.
Machine Learning: Applications in Social Science Research, University of Michigan, Summer 2023 (with Prof. Chris Hare). [Course website]
Institute for Data Science and Big Data, Center for Data Science, American University, Winter 2022 (with Prof. Ryan Moore). [Course website]
Bayesian Statistics for Social and Biomedical Sciences, American University, Fall 2021 (with Prof. Jeff Gill). [Course website]
Introduction to Political Science Research, American University, Spring 2020 (with Prof. Andrew Flores)