Duke University

To save time, please schedule meetings through the appointment link. Multiple slots are available throughout the week and will be updated regularly. If none of the available times work for you, please send me an email and we will find an alternative time. My office is located at 294A Gross Hall.

1. Spring 2026: Introduction to Regression Methods. [temporary syllabus]

2. Fall 2025: Computational Methods in Social Science. [updated syllabus]

   • Lecture 1: Introduction and Logistics
   • Lecture 2: Linear Methods and Regularization
   • Lecture 3: Post-Selection Inference
   • Lecture 4: Tree-based Methods and Boosting
   • Lecture 5: Overview of Unsupervised Learning
   • Lecture 6: Causal Inference and Machine Learning I: HTE
   • Lecture 7: Causal Inference and Machine Learning II: Double Machine Learning
   • Lecture 8: Causal Inference and Machine Learning III: DML and Identification Strategies
   • Lecture 9: Text as Data: Representation and Descriptive Inference
   • Lecture 10: Deep Learning I: Basics and CNN
   • Lecture 11: Deep Learning II: NLP and Transformer
   • Lecture 12: Causal Inference with Latent Treatment, Outcome, and Confounder
   • Lecture 13: Network Analysis I: Statistical Perspectives
   • Lecture 14: Causal Inference under Interference (Guest Speaker: Ye Wang from UNC)
   • Lecture 15: Network Analysis II: Strategic Perspectives

New York University

1. Introduction to Quantitative Political Analysis III (TA, Ph.D. Level)

   • Covering: Bayesian Statistics, Machine Learning, Text Analysis, MLE.

2. Game Theory II (TA, Ph.D. Level)

   • Recitation Note 1: Monotone Comparative Statics and Supermodular Game
   • Recitation Note 2: Equilibrium Refinements in Extensive Form Games

3. East Asian Politics: China & Japan (Master Level)

4. Master’s Thesis Seminar (Master Level)

5. Games, Strategy, and Politics (TA, Undergraduate Level)

6. Introduction to Game Theory (TA, Undergraduate Level)

7. Introduction to Research Methods for Politics (TA, Undergraduate Level)

   • Covering: Programming with R, Causal Inference