Jae Yeon Kim

Jae Yeon Kim

Computational social scientist

UC Berkeley

I am a PhD candidate in Political Science and a D-Lab Senior Data Science Fellow at UC Berkeley. I will be an Assistant Research Scholar at the SNF Agora Institute at The Johns Hopkins University between July and December 2021. I will join the KDI School of Public Policy and Management as an Assistant Professor, starting January 2022.

As a computational social scientist, my research agenda is interdisciplinary and lies at the intersection of social sciences and data science. My current research interests are divided into two streams: (1) data science for social science and (2) social science for data science. For the first research line, I use big data and machine learning to conduct large-scale policy, opinion, and behavioral research, especially among marginalized populations. For the second research line, I use social science to build more fair and inclusive automated decision systems. My dissertation examines the politics of solidarity in multiracial America, and my other research concerns political behavior in the U.S. and beyond.

My research has been published or is forthcoming in academic journals and conference proceedings, including Political Research Quarterly, Studies in American Political Development, Journal of Computational Social Science, and Proceedings of the Fourteenth International Conference on Web and Social Media (ICWSM), Data Challenge Workshop. My research has also appeared in popular outlets such as the Washington Post’s Monkey Cage. I am the recipient of the Western Political Science Association’s 2020 Don T. Nakanishi Award for distinguished scholarship in Asian Pacific American Politics.

I am also a research software developer and data science educator. I have either developed or co-developed six R packages for computationally intensive social science research. I have taught computational social science at both graduate and undergraduate levels in semester-long courses and short workshops. I am currently working on an open textbook project titled “Computational Thinking for Social Scientists."

To get in touch with me, please send me an email at jaeyeonkim@berkeley.edu.

Interests

  • Computational social science
  • Political behavior
  • Racial and ethnic politics
  • Historical social science

Education

  • PhD Candidate in Political Science, 2016-2021 (expected)

    UC Berkeley

  • MA in Political Science, 2016

    UC Berkeley

Peer-reviewed articles

(2020). Intersectional Bias in Hate Speech and Abusive Language Datasets. Proceedings of the Fourteenth International Conference on Web and Social Media (ICWSM), Data Challenge Workshop.

PDF Code Slides

Research briefs

Nathan Chan, Jae Yeon Kim, and Vivien Leung. Thanks to Trump’s Rhetoric, Asian Americans Are Moving Toward the Democratic Party. Washington Post’s Monkey Cage. March 30, 2021

Jae Yeon Kim. The Three Tales of Chinatown: Why Racism Is Not Enough to Create a Race-based Coalition among Marginalized Groups. UC Berkeley Canadian Studies Program. March 29, 2021

Other publications

Kim, Jae Yeon. Why Teaching Social Scientists How To Code Like A Professional Is Important. UC Berkeley D-Lab. September 23, 2020.

Haber, Jaren, Jae Yeon Kim, and Nick Camp. BAY-SICSS: Bridging Computational Social Scientists and Practitioners for Social Good. Berkeley Institute of Data Science. September 15, 2020.

Kim, Jae Yeon. Five Principles to Get Undergraduates Involved in Real-world Data Science Projects. SAGE Ocean. June 24, 2020.

Kim, Jae Yeon. How I Accidentally Became Interested in Data Science. UC Berkeley D-Lab. February 24, 2020.

Software & Data

Here is the list of software and data, which I have created for other researchers and practitioners.

Open-source Software

  1. Stable
  • (a) tidytweetjson: R package for turning Tweet JSON files into a cleaned and wrangled dataset.
  • (b) tidyethnicnews: R package for turning search results from one of the largest databases on ethnic newspapers and magazines published in the United States into a cleaned and wrangled dataset.
  • (c) makereproducible: R package for making a project computationally reproducible before sharing it.
  • (d) rnytapi: R interface for the New York Times API
  1. Still contains known or unknown bugs
  • (a) MapAgora: R package for getting tax reports, websites, and social media handles related to nonprofit organizations in the United States (with Milan de Vries)
  • (b) autotextclassifier: R package for automatically classifying texts based on tidymodels (with Milan de Vries)
  1. Internal use
  • (a) p3themes: R package for applying p3 lab themes to ggplot2 objects (with Grace Park and Liz McKenna)

Public Data

Teaching

Teaching Awards and Training

Graduate Seminars:

Undergraduate Lectures:

  • Graduate student instructor for Laura Stoker, Introduction to Empirical Analysis and Quantitative Methods, Department of Political Science, UC Berkeley, Fall 2016

Workshops:

CV

Please view my CV.

Collaboration

I believe that collaboration makes me a better researcher and research more fun.

I have co-founded three interdisciplinary working groups and co-organized the Summer Institute in Computational Social Science in the San Francisco Bay Area, hosted by UC Berkeley and Stanford, all while in graduate school.

I have conducted research with 18 amazing people across social sciences and engineering. The following is the list of their names, fields, and affiliations (ordered alphabetically). Please check out their websites to learn more about their research.

Bio

I was born and raised in South Korea, but I had also lived in Hong Kong and Taiwan by the time I finished college. I moved to the San Francisco Bay Area in 2014 as a graduate student in political science at UC Berkeley. Before my graduate studies, I worked in the tech industry in South Korea. I was a strategy manager at a software startup and served on the advisory board of Naver, “The Google of South Korea.” I have enjoyed living in different places of the world and love working with people from various backgrounds.

I am a first-generation college student and grew up in a working-class family. My late father was a factory worker of 32 years at a paper mill, and my mother was a housekeeper. Despite their humble educational backgrounds, they loved and valued learning. My supportive parents and numerous mentors I have met throughout my academic career put me on the path to be where I am today. To pay forward the generosity I have received, I am committed to increasing diversity and inclusion in academia. I am happy to chat with students, especially those underrepresented in academia, regarding learning computational methods, applying to graduate schools and jobs, and building an academic career.

When I do not write or code, I enjoy running and cooking. I learned almost everything I need to know about professionalism from reading Haruki Murakami’s What I Talk About When I Talk About Running.