Joshua Grossman

Joshua Grossman is an Assistant Teaching Professor in the Department of Statistics at the University of California, Berkeley. His research focuses on understanding racial disparities in judicial decisions and college admissions policies, leveraging computational social science methodologies. Grossman holds a Ph.D. in Computational Social Science from Stanford University, advised by Sharad Goel, and a B.A. in Neurobiology and Statistics from Harvard University. He previously worked at IXL Learning, a K–12 education technology company. His work has been recognized with awards such as the Best Plenary Talk at the 10th International Conference on Computational Social Science (2024). Grossman is actively involved in expanding access to statistics and data science education, teaching courses like DATA 100 at Berkeley.

His research spans topics including the impact of college admissions policies on Asian American applicants, biases in pretrial detention systems, and the application of algorithmic methods in healthcare and education. Grossman collaborates with institutions to analyze policy implications and has published in venues like the Proceedings of the National Academy of Sciences and Journal of Empirical Legal Studies. His lab’s replication materials, such as those for the study on Asian American admissions, are publicly available on GitHub.