People
Faculty
Jia Miao
Jia Miao is an Assistant Professor of Sociology at NYU Shanghai. Her research examines the impact of urbanization, urban redevelopment, and neighborhoods on social cohesion, health inequality, and subjective well-being in Asia. She also explores the social consequences of homeownership in major Chinese cities using experimental designs. Additionally, she studies the interplay between family and neighborhood dynamics in shaping the well-being of older adults amid rapid population aging in Chinese societies. Her work has been published in Social Forces, Chinese Sociological Review, Social Science & Medicine, Health & Places, Cities, and other journals.
Zixi Chen
Zixi Chen (陳梓曦) is an Assistant Professor of Practice in Computational Social Science at NYU Shanghai, affiliated with the Center for Applied Social and Economic Research (CASER). She is passionate about fostering interdisciplinary collaboration to address societal challenges through computational methods. Trained as a methodologist, her research integrates text-as-data approaches, social network analysis, and quantitative methods to examine human behavior in technology-mediated education and social contexts. She is also dedicated to developing big-and-rich data frameworks that combines the advantages of digital big data and design-based survey data to advance equity in data-driven social science research. Her work has been published in leading journals such as the American Journal of Education and the Journal of Research on Technology in Education.
Yongjun Zhang
Dr. Yongjun Zhang is an Assistant Professor of Sociology and the Institute for Advanced Computational Science at Stony Brook University. He is also affiliated with the Department of Asian and Asian American Studies, the AI Innovation Institute, and the Center for Changing Systems of Power.As a computational social scientist, Dr. Zhang leverages large-scale data, natural language processing, and computer vision to investigate social, political, and organizational behavior, focusing on topics such as racial segregation, political polarization, and organizational inequality.His research has been published in leading journals, including American Journal of Sociology, Demography, Scientific Reports, and Humanities and Social Sciences Communications. He is also the co-editor of Computational Social Science: Applications in China Studies and serves on the editorial boards of several journals, including Nature Scientific Data, Journal of Mathematical Sociology, Socius, Social Science Computer Review, and The Sociological Quarterly.Speakers
Chris Bail
Chris Bail is Professor of Sociology, Computer Science, Political Science, and Public Policy at Duke University, where he directs the Society-Centered AI Initiative and co-directs the Polarization Lab. He studies how artificial intelligence shapes human behavior in a range of different settings—and social media platforms in particular.
Yingdan Lu
Yingdan Lu is an Assistant Professor in the Department of Communication Studies at Northwestern University. She is the director of the Computational Media and Politics Lab, and the co-director of the Computational Multimodal Communication Lab. Her research focuses on digital technology, political communication, and information manipulation. She uses computational and qualitative methods to understand topics like the evolution and engagement of digital propaganda in authoritarian regimes and how individuals encounter and communicate multimodal information in AI-mediated environments. Her work has appeared in peer-reviewed journals such as Proceedings of the National Academy of Sciences (PNAS), Political Communication, New Media & Society, Human-Computer Interaction, and among other peer-reviewed journals. She received her Ph.D. in Communication and a Ph.D. minor in Political Science from Stanford University.
Yongren Shi
Yongren Shi is an assistant professor in the department of sociology and criminology, University of Iowa. His research examines how culture, membership, and social networks contribute to stratification and polarization within human groups. He uses large-scale digital trace data and a range of quantitative and computational methods, including network analysis, computational textual analysis, agent-based modeling, machine learning, online experiments, and survey analysis. Professor Shi’s research has appeared in top sociology and science journals, including American Sociological Review, American Journal of Sociology, and Nature Human Behaviour. His work has been covered by dozens of popular media outlets.
Lingfei Wu
Lingfei Wu is an Assistant Professor of Information Science at the University of Pittsburgh. His research leverages big data, complexity science, and AI to advance the Science of Team Science and Innovation. He has transformed prior research in two key ways: first, by introducing the Disruption Index (D-index) as the first metric for scientific breakthroughs, shifting research evaluation from citation-based popularity to disruptive innovation; and second, by uncovering the hidden costs of team collaboration—how large teams can suppress individual creativity and recognition, thus complementing organizational studies that focus mainly on team benefits. His work provides the scientific foundation for funding innovative small teams, as well as informing organizational and policy changes to balance individual and team success in the age of team-based science. Lingfei’s research is widely recognized in Computational Social Science and the Science of Science, with publications in Nature, PNAS, and other top journals. His work has been featured in major media outlets, including The New York Times, Harvard Business Review, Forbes, The Atlantic, and Scientific American. As a thought leader in research evaluation, he has advised organizations such as the National Institutes of Health (NIH), Novo Nordisk Foundation, and John Templeton Foundation. His research and teaching excellence have been recognized with awards including the NSF CAREER Award, Richard King Mellon Award, and Oxford Martin Fellowship.
Xiaoyang Ye
Xiaoyang Ye is an economist at Amazon. He earned his Ph.D. from the University of Michigan and was a postdoctoral researcher in the economics of education at Princeton and Brown. His research focuses on optimizing human decision-making from education to the workforce through causal machine learning and behavioral economics, with a particular emphasis on using randomized experiments to expand educational opportunities for low-income students.
Teaching Assistants
