Lanu Kim

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About

I am an associate professor in the school of Digital humanities and computational social sciences (Link) and a joint professor in the school of computing (Link) and the graduate school of data science, Korea Advanced Institute of Science and Technology (KAIST). After finishing my sociology PhD at the University of Washington, I was a postdoctoral fellow and data science scholar at Stanford University. My research broadly contributes to the theoretical understanding of academic knowledge creation by mainly examining the impact of academic search engines, gender inequality in higher education, and the social structure of knowledge construction. To investigate, I utilize new big data sources, innovative analytical strategies, natural language processing, and advanced statistical methods and work with interdisciplinary research teams. I am always open to new collaboration opportunities. Please contact me via email if you are interested.

If you’re interested in projects that are under review or have drafts ready, I’d be happy to share — just send me an email.

Research Interest Keywords

Computational Social Science; Science of Science; Sociology of Knowledge; Data Science; Gender; Inequality; Social Network Analysis; Technology and Society

Academic Positions

2024- Associate professor. School of Digital Humanities and Computational Social Sciences, KAIST.

2021- Joint professor, School of Computing, KAIST.

2023- Joint professor, Graduate School of Data Science, KAIST.

2025- Affiliated professor, New York University.

2021-2024 Assistant professor. School of Digital Humanities and Computational Social Sciences, KAIST.

2018-2021 Postdoctoral fellow. Graduate School of Education, Stanford University.

Education

Ph.D. 2018. Sociology, University of Washington.
M.A. 2014. Sociology, University of Washington.
B.A. 2007. Sociology and economics, Seoul National University.

Selected Publications

Lanu Kim, Bas Hofstra, and Sebastian Galvez. 2024. “A Persistent Gender Pay Gap Among Faculty in a Public University System.” Scientific Reports 14(22212). Link

Lanu Kim, Sanne Smith, Linus Dahlander, and Daniel A. McFarland. 2024. “Networking a career: Individual adaptation in the network ecology of faculty.” Social Networks 77(166-179). Link

Lanu Kim, Daniel Scott Smith, Bas Hofstra, and Daniel A. McFarland. 2022. “Gendered Knowledge in Fields and Academic Careers.” Research Policy 51(1). Link

Lanu Kim, Christopher Adolph, Jevin West, and Katherine Stovel. 2020. “The Influence of Changing Marginals on Measures of Inequality in Scholarly Citations: Evidence of Bias and a Resampling Correction.” Sociological Science 7:314-341. Link

Lanu Kim, Jason Portenoy, Jevin West, and Katherine Stovel. 2020. “Scientific Journals Still Matter in the Era of Academic Search Engines and Preprint Archives.” Journal of the Association for Information Science and Technology 71(10):1218-1226. Link

Projects and Publications/Working Papers

Technology and society

From my PhD journey at the University of Washington to my time at KAIST, I’ve felt a strong societal need to study rapidly evolving technologies and their social impacts. Researching constantly developing technology is challenging — like aiming at a moving target — but I believe it’s one of the most essential tasks for contemporary sociologists. Using computational methods, ethnography, interviews, and surveys, I explore technological change and its implications for social inequality.

Myokyung Han, Jiwoon Hong, Taegyoon Kim, Jinhyuk Yun, and Lanu Kim. “Uneven Automation: ChatGPT’s Impact on Software Tasks Varies by Difficulty and Data Availability.” [draft ready] (Will be presented in IC2S2 2025 and ASA 2025)

Dujin Park, Suh-young Choi, Lanu Kim, and June Jeon. “Artificial Intelligence and the New Divide: Perceptions, Preparedness, and the Future of Labor Market Inequality.” [draft ready]

Jeon, June, Lanu Kim, Jaehyuk Park. 2025. “The Ethics of Generative AI in Social Science Research: A Qualitative Approach for Community-Based AI Research Ethics.” Technology in Society 81(102836). Link

Hansen, Ryan N., Basil Matthew Saour, Brian Serafini, Blake Hannaford, Lanu Kim, Takayoshi Kohno, Ryan James, Wayne Monsky, and Stephen P. Seslar. 2021. “Opportunities and Barriers to Rural Telerobotic Surgical Health Care in 2021: Report and Research Agenda from a Stakeholder Workshop.” Telemedicine and e-Health Published online. Link

Serafini, Brian, Lanu Kim, Basil M. Saour, Ryan James, Blake Hannaford, Ryan Hansen, Tadayoshi Kohno, Wayne Monsky, and Stephen P. Seslar. 2022. “Exploring telerobotic cardiac catheter ablation in a rural community hospital: A pilot study.” Cardiovascular Digital Health Journal 3(6): 313-319. Link

Lanu Kim. 2021. “Geographical Locations of Occupations and Information and Communication Technology: Do Online Tools Impact Where People in the U.S. Live and Work?” Sage open. Link

Knowledge structure with a global perspective

Where I stand shapes my perspective and research interests, and this stream of research reflects my standpoint as a researcher located in a non-western higher eudcation system. I draw implications of this global structure in our scientific knowledge-making process with a critical perspective backed by data-driven analyses.

Maida Aizaz, Taekho You, June Jeon, Lanu Kim. “From Affiliation to Attribution: Who Mentions Whom in Science?” [working paper] (Will be presented in ICSSI 2025)

June Jeon, Byungjun Kim, Maida Aizaz, Suhyoung Choi, Lanu Kim. “Peripheralization through Knowledge: Rethinking Social Science via Global Value Chains.” [working paper]

Lanu Kim, Sue-yeon Song. 2020. “Is Korean Academia Unique?: Comparison of Knowledge Discourses between Korean and International Sociology.” Korean Journal of Sociology 54(4):1-40. (in Korean) Link

Gender in academia and professional jobs

As a postdoctoral fellow at Stanford, I have been involved in projects that study the role of gender in the process of knowledge creation. I began a project to investigate whether gendered research interests contribute to women’s underrepresentation in the professoriate. I extend these interests by examining both gender differences in research topical interests and the gender pay gap across various national contexts.

Lake Lui, Lanu Kim. “Gender inequality in academia: a longitudinal study of South Korea and Taiwan (2008-2023).” [working paper]

Minyoung Choi, Myokyung Han, Suhyoung Choi, Eunhee Bae, Dongju Kim, Bong Gwan Jun, Lanu Kim. “Greedy Leadership Roles: Why Women in Engineering Leadership Are Rare.” [under review]

Lanu Kim, Bas Hofstra, and Sebastian Galvez. 2024. “A Persistent Gender Pay Gap Among Faculty in a Public University System.” Scientific Reports 14(22212). Link

Lanu Kim, Daniel Scott Smith, Bas Hofstra, and Daniel A. McFarland. 2022. “Gendered Knowledge in Fields and Academic Careers.” Research Policy 51(1). Link

Risi, Stephan, Mathias W. Nielsen, Emma Kerr, Emer Brady, Lanu Kim, Daniel A. McFarland, Dan Jurafsky, James Zou, and Londa Schiebinger. 2022. “Diversifying history: A large-scale analysis of changes in researcher demographics and scholarly agendas.” PloS one 17(1): e0262027. Link

Gender in LLM

As more students become interested in exploring potential amplified gender inequalities in large language models (LLMs), I have started a few projects that study or utilize LLMs to examine existing inequalities in our society. Here are projects currently in development.

Wenchao Dong, Lanu Kim, Mia Cha. “Algorithmic Implications of Gender Wage Gap and Stereotypes in the Tech Sector.” [under review]

Social and conceptual networks in academia

I study how scientific knowledge evolves by extracting concepts from text and collaboration networks using natural language processing and social network analysis. This set of articles reflects that research focus.

Youjin Hong, Byungjun Kim, June Jeon, and Lanu Kim. “Has higher education been more interdisciplinary?: evidence from longitudinal analysis using natural language processing on syllabi.” [under review]

Lanu Kim, Sanne Smith, Linus Dahlander, and Daniel A. McFarland. 2022. “Networking a career: Individual adaptation in the network ecology of faculty.” Social Networks (online). Link

Lanu Kim, Vivek Kulkarni, Daniel McFarland. “Modeling Tie Dynamics in Ideational Spaces of Scientific Fields - A Network Theoretic Approach.” [draft ready]

Data science for law and crime

We all talk about AI judges and lawyers, but have we seen a reliable model? Legal text data is highly unstructured, and everyday legal practice doesn’t always fit mathematical models. I study law and crime, exploring how data science can be applied in this field and examining what AI can — and cannot — do.

Youjin Hong and Lanu Kim. “Public Attention and Judicial Consistency: How the Public Drives Lower Court Judges’ Adherence to Guidelines.” [draft ready]

Kyungjong Kim and Lanu Kim. “The intersection of data science and organizational initiatives in voice phishing prevention.” [working paper]

Echo chambers in science?

While pursuing my Ph.D., I examined how technology has shaped our work practices among academics by focusing on the ways in which scientists engage with prior scientific literature. My dissertation idea was developed into an NSF-funded project, “Echo Chambers in Science” (Link). This project, which grew out of conversations I initiated with my advisor, investigated how the development of integrated academic search engines like Google Scholar may have transformed the citation behavior of researchers across multiple scientific fields.

Lanu Kim, Christopher Adolph, Jevin West, and Katherine Stovel. 2020. “The Influence of Changing Marginals on Measures of Inequality in Scholarly Citations: Evidence of Bias and a Resampling Correction.” Sociological Science 7:314-341. Link

Lanu Kim, Jason Portenoy, Jevin West, and Katherine Stovel. 2020. “Scientific Journals Still Matter in the Era of Academic Search Engines and Preprint Archives.” Journal of the Association for Information Science and Technology 71(10):1218-1226. Link

Korean society

Daeun Kwan, Seulki Choi, and Lanu Kim. “Exploring Community-Level Childcare Social Networks: A Comparative Mixed Methods Study of South Korea.” [under review]

Yoon, Soo-Yeon, Sojung Lim, and Lanu Kim. 2021. “Labour Market Uncertainty and the Economic Foundations of Marriage in South Korea.” Asian Population Studies Published online. Link

Shin, Solee and Lanu Kim. 2020. “Chaebol’s Turn to Service: Rise of a Korean Service Economy and the Dynamics of Self-Employment and Wage Work.” Journal of Contemporary Asia 50(3):433-456. Link

Shin, Solee and Lanu Kim. 2013. “Organizing K-pop: Emergence and Market Making of Large Korean Entertainment Houses, 1980-2010.” East Asia 30(4):255-272. Link

Chang, Dukjin, Lanu Kim, and Kiwoong Park. 2012. “The Political Economic Approach on Voting Behaviors in the 17th Korean Assembly Using NOMINATE Analysis.” Korean Journal of Sociology 46(1):1-23. [In Korean] Link

Lanu Kim. 2010. “A Study of Change in Residence Stability through Analyzing Home-Ownership Rates: A Case Study in Seoul, Republic of Korea, 1985-2005.” Seoul Studies 11(1):43-59. [In Korean] Link

Awards

Grants

Services

Editorial board member

Journal reviews

Conference reviews

SICSS organizer

Agenda on Teaching and Research

As a sociology Ph.D. participating in multiple projects rooted in big data and computational methods, I present three elements of my agenda that I bring to my teaching and research.

  1. I believe the future of scholarship lies in interdisciplinary research. From my academic experience, I recognize that advances in data science have the potential to unlock enormous opportunities that will require both excellent data management/analysis expertise and the ability to ask key research questions. Thus, one primary motivation for my work lies in fully exploring these research opportunities.

  2. I have found a unique role for sociology in data science research pursued by such interdisciplinary teams. Sociology offers abundant theoretical scaffolding, which helps explain underlying organizational processes found in big data. My research and mentorship provide an insightful perspective that can guide big data research to have both theoretical and substantive meanings in society, which strengthens my niche in interdisciplinary research.

  3. I am committed to encouraging gender and racial/ethnic minority scholars to utilize data science. While computational methods are shaping many new research directions in the social sciences, ingrained cultural beliefs concerning gender have frequently and consistently discouraged women from pursuing math-based career trajectories.

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