Teaching and advising
My teaching style is effective because it combines my academic expertise with professional experience.
I am an award-winning certified instructor and have taught computational social science in semester-long courses and short workshops.
Pedagogical resources
- Original textbook: “Computational Thinking for Social Scientists.”
- “Teaching Computational Social Science for All.” (with Margaret Ng)
- “Training Computational Social Science Ph.D. Students for Academic and Non-Academic Careers.” (with Aniket Kesari, Sono Shah, Taylor Brown, Tiago Ventura, and Tina Law)
Potential future teaching
- American Politics
- Public Policy and Organizations
- Political Behavior and Psychology
- Racial and Ethnic Politics
- Urban and Local Politics
- Quantitative Methods
- Computational Social Science
Courses taught
Graduate seminars
KDI School
- R Fundamentals for Public Policy (Spring 2022, Fall 2022 [2x])
- Data Visualization and Communication (Summer 2022)
UC Berkeley
- An Introduction to Computational Tools and Techniques for Social Science Research
- Instructor of record: Spring 2019, Spring 2021 [2x]
- Graduate student instructor with Rachel Bernhard: Fall 2016
- Research Workshop on Computational Tools for Digital Data Collection (with Nick Kuipers: Fall 2020)
Undergraduate lectures
UC Berkeley
- Introduction to Empirical Analysis and Quantitative Methods (with Laura Stoker: Fall 2016)
- Received the Outstanding Graduate Student Instructor Award
Pre-conference tutorials
- International Conference for Computational Social Science (IC2S2 ’24, July 2024): Training Computational Social Science PhDs for Academic and Non-academic Careers
- Summer Institute in Computational Social Science-Howard R Bootcamp (SICSS-Howard, June 2021). Invited instructor
Workshops
- Berkeley Interdisciplinary Migration Initiative: Summer Institute in Migration Research Methods (Summer 2024). Invited instructor
- Korea University Institute of Politics: Digital Data Collection (Summer 2022). Invited instructor
- UC Berkeley D-Lab (Summer 2020-Summer 2020):
- Fairness and Bias in Machine Learning
- Machine Learning in R
- SQL for R Users
- R Package Development
- Functional Programming in R
- Advanced Data Wrangling in R
- Reproducible Project Management in R
- R fundamentals
Guest lectures
- 2023: Wesleyan (Challenges to Democracy in East Asia)
- 2022:
- National University of Singapore (Digital Communications and Analytics)
- KAIST (Data Analysis for Green Business and Policy, Science, Technology, and Society)
- Korea University (Introduction to Political Science)
- Sungkyunkwan University (Methods of Economic Data Analysis)
- 2021:
- KAIST (Data Analysis for Green Business and Policy)
- Dartmouth (Experiments in Politics)
