Selected Courses and Training

Quantitative Methods

PhD Level:
Causal Inference
Bayesian Modeling for Social Science I & II
Multilevel Models
Randomized Controlled Trials Design
Advanced Linear Regression

Master Level:
Introduction to Spatial Data Science
Survey Research Methodology
Social Network Analysis

Data Science

PhD Level:
Human-Centered Machine Learning

Master Level:
Natural Language Processing
Large-Scale Computing for the Social Sciences

Undergraduate Level:
Machine Learning
Computer Vision

Programming

Master Level:
Python Programming I & II

Undergraduate Level:
Statistical Computing (R)
Introduction to C++

Data Engineering

Master Level:
Databases
Cloud Computing

Social Science and Public Policy

Master Level:
Epidemiology and Population Health
Economic Development and Policy
Energy in the Developing World

Undergraduate Level:
Intermediate Microeconomics Theory
Intermediate Macroeconomics Theory
Advanced Public Economics
Personnel Economics
Environmental and Resource Economics
Modeling Political Process
Political Economy of Development
Chinese Politics

Mathematics and Statistics

Undergraduate Level:
Calculus I & II & III
Linear Algebra
Probability
Theoretical Statistics
Computational Methods (Simulation-Based Inference)