Buxin Su

I am a Ph.D. student in Applied Mathematics and Computational Science at the University of Pennsylvania, where I have worked with Weijie Su and Bingxin Zhao. Before that, I received an M.S. in Pure Mathematics from Penn and a B.S. in both Pure Mathematics and Applied Mathematics from the University of Toronto, graduating with High Distinction.

Portrait of Buxin Su

My research lies at the intersection of statistics, machine learning, and the social sciences. I develop statistical theory and methods to address three connected challenges: improving how the AI community evaluates research, increasing the utility of social data under privacy constraints, and making statistical learning more reliable for biomedical applications. My work combines theoretical analysis, empirical experiments, and practical implementation. Contributions include methods incorporated into the ICML 2026 review process, methods that reduce noise variance in the 2020 U.S. Census by about 20% while maintaining the same level of privacy protection, and studies of the statistical foundations of biomedical prediction.

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