Short bio

Garrett G. Wen is a Ph.D. candidate in Statistics and Data Science at Yale University. His doctoral advisors are Theodor Misiakiewicz and Zhou Fan, and Weijie Su has advised his research since 2021. His research lies at the intersection of high-dimensional statistics, probability theory, deep learning, generative AI, and optimization. He studies mathematical descriptions of modern learning systems, including non-universality in random features, dynamical mean-field limits, random geometric constraint problems, language-model watermarking, and mechanisms for scientific peer review.

He received an M.A. in Statistics from Yale University in December 2025 and a B.Sc. in Statistics from Peking University in 2022. He was a Summer Research Associate at the Flatiron Institute's Center for Computational Mathematics in 2026, advised by Alberto Bietti, Nikhil Ghosh, and Denny Wu. Previously, he conducted research at the Wharton School of the University of Pennsylvania, MEGVII, and Peking University.

His CV is available here (updated October 2026).

Education