Teaching

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 and received the Outstanding Graduate Student Instructor Award for my instruction in undergraduate quantitative methods and research design. This award is given to fewer than ten percent of graduate student instructors at UC Berkeley.

Scheduled Teaching in Public Policy (UNC-CH)

The Politics of Public Policy

This course starts from the idea that power and politics are central to policy innovation, because innovation disrupts the status quo. Drawing on research from political science, sociology, psychology, economics, public administration, and management, the course examines how institutions, organizations, and individuals shape how policies are delivered, and why this process is inherently political. Students will engage with frameworks that conceptualize how people experience government on the ground, and how those experiences both reflect and reshape the politics, power structures, and institutions behind them. The course prepares students to design policy innovations that reduce barriers to access and to scale those innovations across programs and agencies.

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

The following three courses form an integrated sequence in public policy data science, grounded in a domain-first approach. Rather than treating data science as a purely technical endeavor, the sequence begins with the policy problems themselves—emphasizing how data, computation, and artificial intelligence can be used in context to advance the public good. Each course explores a distinct but complementary dimension of data-informed governance—from algorithmic decision-making to digital service delivery and researcher-practitioner collaboration. Together, they prepare students to critically and creatively engage with the expanding role of data and AI in the policy process, equipping them with the analytical skills, institutional understanding, and ethical grounding needed to design, implement, and evaluate data- and AI-driven solutions to public problems.

  • Data, Algorithms, and AI for Public Policy

This course introduces students to the promises and risks of data-driven, algorithmic, and AI-enabled decision-making in the public sector. Through cases in social policy, criminal justice, and public health, students critically assess how algorithms and machine learning models are used to allocate resources, automate decisions, and influence policy outcomes. Particular attention is given to the unique challenges posed by artificial intelligence, including issues of opacity, bias, and institutional accountability. The course emphasizes frameworks for evaluating and designing algorithmic and AI systems that are fair, transparent, and aligned with the goals and constraints of public governance.

  • Civic Tech and Digital Public Service

This course examines how public agencies use technology, data, artificial intelligence, and human-centered design to deliver more accessible, efficient, and equitable services. Students explore the role of digital platforms—such as websites, mobile applications, chatbots, and AI-powered tools—in shaping citizen experiences with government. The course highlights civic technology approaches rooted in participatory design and administrative burden reduction, while also addressing emerging questions about the use of AI in service delivery, including transparency, user autonomy, and algorithmic accountability. Emphasis is placed on how digital and AI-enabled tools can be designed to enhance—not replace—human judgment, trust, and inclusion in public programs.

  • Evidence in Action: Partnering for Public Impact

This course examines how data scientists and policy researchers can work in partnership with public sector organizations to build actionable evidence and improve service delivery. Students learn to scope policy-relevant questions, design and implement applied analyses, and communicate results to diverse audiences and stakeholders. The course introduces the SIMC framework (Scoping, Implementation, Measurement, and Communication) as a practical guide for navigating the tensions between research rigor and institutional constraints. Through case studies in safety net programs and other public systems, students explore how political, organizational, and ethical factors shape the collaborative process and use of evidence for public impact.

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

  • Textbook: Computational Thinking for Social Scientists
    An open-access textbook (CC BY‑NC‑SA 4.0) that equips social scientists with modern computational skills: reproducible workflows (Git, Bash, tidy data), functional programming in R, data product development, semi-structured data collection, computational text analysis, predictive modeling, and database management with SQL. Each modular chapter features live code, interactive exercises, and practical examples—all freely available for adaptation, classroom use, and self-learning.

  • Article: Training CSS PhDs for Academic and Non-Academic Careers, PS: Political Science & Politics (2024)

  • Article: Teaching Computational Social Science for All, PS: Political Science & Politics (2022)

    • PDF views: 911 | HTML views: 1,204 (as of October 31, 2024)
    • Co-authored with Margaret Ng

Computational Social Science Professionalization Process (Kesari, Kim, Shah, Brown, Ventura, and Law 2024: 102).

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