Lab Members

Charles Gomez (He/Him) is the director of the Global Knowledge Lab and Observatory. He studies the rising inequality in global scientific knowledge production and diffusion. He uses topic models, social network analysis, and simulations. He focuses on hierarchies, diversity, complexity, and novelty in his work. 

His work has been featured in Nature Human Behaviour, Nature Communications, Research Policy, Social Networks, Journal of Informetrics, and Sociological Science. 

He’s an associate professor at the University of Arizona’s School of Sociology and is affiliated with the College of Information Science and the Applied Math Graduate Interdisciplinary Program (GIDP).

He’s the recipient of a National Science Foundation (NSF) CAREER Award (2024-2029) to study how international politics shapes the field of artificial intelligence (AI) and its researchers. He has received over a million dollars in grants as the PI or co-PI.

Before Arizona, he was an assistant professor at the City University of New York, Queens College’s Department of Sociology. He was also a lecturer and data science postdoctoral researcher at the University of California, Berkeley’s School of Information. 

He received his Ph.D. from Stanford, his master’s degrees from the Harvard Kennedy School and Columbia, and his B.Sc.Eng. from Duke.

Learn more at his website: https://www.charliegomez.com

Jina Lee is an assistant professor at the University of Illinois, Urbana-Champaign’s Department of Sociology. She is a sociologist who explores the processes and consequences of valuation: the social process through which importance, worth, or significance is assigned to ideas, practices, or contributions. She focuses on which novel discoveries are viewed as important in science, and how this affects scientific progress. She also examines how gender and race shape decisions of what is important, and how this reinforces existing inequality. Her work contributes to the sociology of science and knowledge, gender, medical sociology, and culture. She employs diverse methods such as computational text analysis and survey-based experiments. Her research is published in American Sociological Review, Socius, and Journal of Social Entrepreneurship.

Learn more at her website: www.jinalee.org

Jeffrey Shen is a Ph.D. candidate in the School of Sociology at the University of Arizona who studies social psychology, social networks, and methodology. His broad research agenda is to empirically study who takes what for granted. As part of this, his dissertation examines the mismatch between students’ academic perception and performance using surveys, networks, and quasi-experiments.

Jeffrey has a strong methodological background following his extensive quantitative and research-design training at the University of Arizona and Academia Sinica.

Prior to his doctoral study, he trained as a research assistant in a joint research group between the Institute of Statistical Science and the Institute of Sociology, Academia Sinica. He helped develop and analyze novel contact diary data, focusing on identifying the relational determinants behind the unequal distribution of instrumental support and emotional support as well as untangling the multi-layered social structure of online and offline interactions. He earned his B.A. in Sociology – Law and Society from the University of California, San Diego (UCSD) in 2017.

Yeaeun Kwon (She/Her) is a Ph.D. student in Information Science at the University of Arizona. Her research interests lie in Computational Social Science. Specifically, she focuses on human and human interactions, social biases, and dynamics on social media by employing NLP techniques. Prior to her doctoral studies, she researched string pattern matching algorithms for her master’s thesis, comparing short and long-term data. She received an M.S. in Computer Engineering from Korea Aerospace University and a B.A. in Economics from Hongik University in South Korea.

Learn more information on her website: https://yeaeunkwon.github.io/

Alan Kang is a Ph.D. student in the School of Information at the University of Arizona. He studies how technology, policy, processes, and people interact in support of talent management and workforce development initiatives.

His current focus is the diffusion and adoption of AI. He is researching the use of AI-accelerated workflows, the effect of AI on the structure of teams, and upskilling and reskilling an AI-ready workforce.

He received an M.S. in Data Science from the University of Arizona and a B.A. in Economics from the University of California, Irvine.

Rishabh Bhonsle is pursuing a master’s in Data Science at the School of Information, University of Arizona. He is a member of the Global Knowledge Lab and Observatory, where he currently serves as a Data Scientist. He works on harnessing information from online databases and will be working to build and code structured databases to contribute to the lab’s mission of studying science and knowledge on a global scale.

His interests lie in the field of computational social sciences, particularly in exploring the organization, diffusion, and translation of knowledge through data. He received a BS-MS (Dual Degree) in Electrical Engineering and Computer Science from the Indian Institute of Science Education and Research, Bhopal.

Silvan Baier is an Empirical Research Fellow at Northwestern University (Kellogg School of Management) and a member of Arizona’s Global Knowledge Lab and Observatory. He studies the role of social structure in the organization, diffusion, and translation of knowledge. He uses natural language processing, network analysis, and computational models to explore these topics.

Prior to Northwestern and Arizona, he conducted research at Yale, USC, and ETH Zurich (Switzerland).

He received an M.A. in Computational Social Science from the University of Chicago, a B.A. in Business Administration from the University of St. Gallen (Switzerland), and was a visiting student at the University of Southern California.

Find more information on his website: https://sites.google.com/view/baier/home

Harshvardhan Singh is a staff member at the University of Arizona, where he is currently a data scientist for The Global Knowledge Lab and Observatory, collaborating with a talented team of researchers.

He performs analyses to understand the evolving dominance and influence of countries across various academic fields over time. He utilizes advanced methodologies to uncover historical trends in research publications and analyze patterns in international collaborations. His responsibilities include data extraction, exploratory analyses, topic modeling, and conducting network analysis, all while ensuring data integrity and consistency throughout the entire data pipeline.

Prior to his current position, he was a graduate student pursuing his M.S. in Data Science at the University of Arizona, where he was associated with the Lab as a research assistant.

Minyoung An is a PhD candidate in the School of Sociology at the University of Arizona. Her research examines how gender, both as an individual attribute and as an institutional force, shapes decision-making processes and in turn, perpetuates social inequality. She investigates this intersecting multilevel force to explore a spectrum of individual choices—from explicit life path decisions such as migration and educational pursuits, to implicit processes of identity formation, including political partisanship and ideologies. To explore these questions, she deploys a wide variety of methods, including quantitative methods, computational methods, and in-depth interviews.

Learn more information on her website: https://an-minyoung.github.io

Ian Pérez (He/Him) is a senior at the University of Arizona pursuing a dual degree: a B.S. in Mathematics and a B.A. in Spanish Translation and Interpretation. His research interest is quantitative social science, specifically in how social structures affect individual economic and health outcomes across racial, gender, and class lines.