Teaching and advising

My teaching style is effective because it combines my academic expertise with professional experience.

As a teacher, I have extensive experience teaching computational social science / applied data science. I have designed and taught four graduate-level courses on computational social science and received the Best Graduate Student Instructor Award for teaching research design and quantitative methods from UC Berkeley. I have co-authored two articles on teaching computational social science and preparing students for diverse careers in data science. I have also been invited to write a book chapter on teaching computational social science, which is co-edited by Matti Nelimarkka (University of Helsinki) and Friedolin Merhout (University of Copenhagen) and is under contract with Edward Elgar Publishing.

As a mentor, I am committed to providing opportunities for historically underrepresented students. Since 2018, I have mentored twelve undergraduate and two graduate students of color in social sciences and STEM fields, 75% of whom are female. Many of these students have gone on to have successful careers in tech, finance, bio, consulting, and the public sector.

As a faculty member, I will continue to advocate for equity, diversity, and inclusion in academia and beyond through my research, teaching, and mentoring. I am eager to participate actively in groups that support students of color, international students, and first-generation college students.

Pedagogical resources on computational social science/applied data science

Capstone project mentoring experience

I have guided undergraduate and graduate students in applied data science projects. In Spring 2020, I was a Data Science Education Program Fellow for the Data Science Discovery Program at UC Berkeley, one of the largest data science research incubator initiatives in the U.S. During this time, I worked on over 40 applied data science projects, ensuring their quality and collaborating with community partners and undergraduate research assistants. An article I authored on involving undergraduates in real-world data science projects was published in SAGE Ocean, a branch of SAGE publishing for computational social science.

Potential future teaching

  • The Politics of Public Policy: This course prepares students to understand the political contexts in which policies are formulated and implemented. We focus on three specific contexts: institutional, behavioral, and organizational. These include political institutions (e.g., the separation of powers, federalism, and decentralization), political behavior (e.g., the media and the mass public), and political organizations (e.g., bureaucracy).

  • Public Policy Making: This course prepares students to understand how policy is formulated, implemented, and evaluated within the context of competing political frames, groups/stakeholders, and their interests. We will analyze this process through five specific policy case studies: housing, education, immigration, poverty and inequality, and criminal justice.

  • U.S. Social Policy: This course prepares students to understand the historical development and contemporary politics surrounding social policy in the United States. It pays particular attention to implementing safety net programs and examines the strengths and limitations of policy innovations designed to address these issues.

  • Data Science for Public Policy: This course is designed to help students develop both computational and communication skills to transform data into compelling, fact-based arguments that can be used in policy design, implementation, and evaluation. The first half of the course focuses on building computational skills necessary for wrangling, modeling, and visualizing large, complex, and often messy datasets. In the second half, students will apply these skills to create data-driven policy products, such as six-page policy briefs.

  • Data and Algorithms for Public Policy: This course prepares students to understand how policies are designed and implemented through the interaction of data and algorithms. We first distinguish between the hype and reality of using data and algorithms across various public policy domains, ranging from social policy to criminal justice, underscoring their positive and negative human consequences. We then discuss how we can use these tools fairly, transparently, and responsibly through effective institutional and organizational design.

  • Civic Tech and Digital Public Service: This course prepares students to understand how technology, design, and data transform public services. Increasingly, citizens interact with government agencies through digital platforms (e.g., websites, mobile applications, etc.). We examine how human decisions create these “tech” products within specific institutional and organizational contexts. We then discuss “civic” tech approaches: design thinking-guided and data-driven methods to reduce administrative burdens in these policy experiences and improve policy access, processes, and outcomes.

Courses taught

Graduate seminars

KDI School

UC Berkeley

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

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):
    1. Fairness and Bias in Machine Learning
    2. Machine Learning in R
    3. SQL for R Users
    4. R Package Development
    5. Functional Programming in R
    6. Advanced Data Wrangling in R
    7. Reproducible Project Management in R
    8. R fundamentals

Guest lectures

  • 2024: Johns Hopkins (Democracy by the Numbers, scheduled)
  • 2023: Wesleyan (Challenges to Democracy in East Asia)
  • 2022:
    1. National University of Singapore (Digital Communications and Analytics)
    2. KAIST (Data Analysis for Green Business and Policy, Science, Technology, and Society)
    3. Korea University (Introduction to Political Science)
    4. Sungkyunkwan University (Methods of Economic Data Analysis)
  • 2021:
    1. KAIST (Data Analysis for Green Business and Policy)
    2. Dartmouth (Experiments in Politics)

Lightening talk on teaching computational social science. Summer Institute in Computational Social Science (2019 Princeton)
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