Teaching

“Human nature is not a machine to be built after a model, and set to do exactly the work prescribed for it, but a tree which requires to grow and develop itself on all sides, according to the tendency of the inward forces which make it a living thing.”

– John Stuart Mill, On Liberty (1859), Chapter 3

I am committed to providing excellent and accessible learning experiences for students at both the undergraduate and graduate levels. In 2021, I earned a Certificate in Teaching and Learning in Higher Education from UC Berkeley. In 2017, I received the Outstanding Graduate Student Instructor Award for my instruction in undergraduate quantitative methods and research design. Given to fewer than ten percent of graduate student instructors, the award recognized the quality of my teaching even in my first semester in the classroom.

My pedagogical approach combines liberal arts–style broad intellectual foundations with design school–style hands-on experiences. My strength as an instructor comes from my interdisciplinary training and extensive applied experience. My objective is to help students build strong intellectual foundations while simultaneously developing the habits of mind that allow them to tackle new problems. Guided by these principles, my teaching in public policy and data science emphasizes engagement with big ideas and cutting-edge scholarship, alongside sustained attention to practical applications and ethical implications. In so doing, I aim to help my students become scientists, designers, and engineers of organizations and institutions that serve the public interest.

Current Teaching in Public Policy (UNC-CH)

Potential Future Courses at the Intersection of Public Policy and Data Science (UNC-CH)
  • AI Policy and Power Architecture

This course introduces students to the framework of power architecture and its relationship to the design and implementation of AI policy through readings, discussions, and hands-on, project-based exercises. In policy, certain outcomes are achieved while others are not. Certain issues make it onto the policy agenda while others do not. Understanding these differences requires us to examine who has the power to make decisions and what institutional arrangements and organizational configurations structure these decision-making processes.

The course is divided into three stages: the setup (introducing the power architecture framework), the design (building policy prototypes), and the testing (stress-testing and learning). Students will form small groups, select their own AI use cases, and determine what kinds of risks they are willing to tolerate and to what extent. They will then examine what institutional arrangements and organizational vehicles are needed to achieve their goals and whether their proposed plans are feasible given the policy, implementation, and political contexts they will face.

This is a deeply intellectual course, but it also has a practical goal: helping policy students connect their interests to technical domains and data science students apply their technical knowledge to policy problems.

Courses Taught (UC Berkeley, KDI School, etc)

Graduate Seminars

KDI School of Public Policy and Management
UC Berkeley

Undergraduate Lectures

  • Introduction to Empirical Analysis and Quantitative Methods (UC Berkeley, Fall 2016)
    • GSI for Laura Stoker
    • Outstanding Graduate Student Instructor Award
Pre-conference Tutorials
Workshops (Selected)
  • Doing Social Science with Generative AI: Research Design, Practical Tools, and Ethical Considerations, Korea University (2025) – Invited Instructor
  • Summer Institute in Migration Research Methods, UC Berkeley (2024) – Invited Instructor
  • Digital Data Collection, Korea University (2022) – Invited Instructor
  • UC Berkeley D-Lab (2020–2021):
    • Fairness and Bias in Machine Learning
    • Machine Learning in R
    • SQL for R Users
    • Functional Programming in R
    • R Package Development
    • Advanced Data Wrangling
    • Project Management in R
    • R Fundamentals
Guest Lectures (Selected)
  • 2025 – Johns Hopkins: NLP for CSS, Brown: Watson Institute’s GPD Module, Korea University
  • 2024 – Johns Hopkins: Democracy by the Numbers
  • 2023 – Wesleyan: Democracy in East Asia
  • 2022 – National University of Singapore, KAIST, Korea University, Sungkyunkwan University
  • 2021 – KAIST, Dartmouth
Pedagogical Publications and Resources

Explaining how to teach computational social science. Photo credit: Princeton Summer Institute in Computational Social Science (2019).
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