Siwei Cheng | 程思薇
Associate Professor of Sociology
Princeton University
William T. Grant Scholar 2023–2028
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All drawings on this site are human-made — by me, not by silicon. They’re not fancy, but no GPUs were harmed in the making of them.
Bio
Siwei Cheng received her Ph.D. in Sociology and Public Policy (2015) and M.A. in Statistics (2012) from the University of Michigan, where she was also trained at the Population Studies Center. She received her B.A. in Economics and Mathematical Statistics from Peking University in 2009. Prior to joining the faculty at Princeton, she was a faculty member at New York University from 2016 to 2026, most recently as an Associate Professor of Sociology. She previously served as an Assistant Professor of Sociology at the University of California, Los Angeles (2015–2016).Cheng's research encompasses various areas of inequality, mobility, labor market, and quantitative methodology. Her work employs a broad array of methodological approaches, including causal inference, panel data methods, discrete choice models, network analysis, machine learning, and survey experiments. Cheng's work has been published in leading social science and general science journals, including the American Journal of Sociology, American Sociological Review, and the Proceedings of the National Academy of Sciences (PNAS). She was awarded the 2025 William Julius Wilson Early Career Award by the Inequality, Poverty, and Mobility Section of the American Sociological Association.
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Current Research
I am currently working on the following projects:(1) Analyzing the inter-connectedness of jobs, occupations, and skills.
(2) Building a large dataset of time-stamped, geo-tagged images to study social dynamics in public spaces.
(3) Combining human mobility data and Census/administrative data to study connections between individuals and communities.
(4) Studying inequality in the production of knowledge through two decades of digitized social science conference programs.
(5) Combining machine learning and causal inference to understand intra- and inter-generational mobility.
See HERE for details on active research projects.
