Manifold fitting-Yao

Thời gian: 10:00 đến 11:00 Ngày 06/04/2026

Địa điểm: A502, Viện Nghiên cứu cao cấp về Toán

Speaker: Zhigang Yao, National U of Singapore

Short bio: Zhigang Yao is an Associate Professor in the Department of Statistics and Data Science at the National University of Singapore (NUS). He also holds a courtesy joint appointment with the Department of Mathematics at NUS. He is a Faculty Affiliate of the Institute of Data Science (IDS) at NUS. He received his Ph.D. in Statistics from University of Pittsburgh in 2011. His thesis advisors are Bill Eddy at Carnegie Mellon and Leon Gleser at University of Pittsburgh. He has been an Assistant Professor at NUS from 2014-2020. Before joining NUS, he has been working with Victor Panaretos as a post-doc researcher at the Swiss Federal Institute of Technology (EPFL) from 2011-2014.

From 2022, he has been a member of the Center of Mathematical Sciences and Applications (CMSA) at Harvard University.  At Harvard, he collaborates with Shing-Tung Yau on manifold fitting and researches the interface between statistics and geometry. He proactively promotes emerging research directions at the intersection of statistics and geometry on an international scale. Notably, he initiated the first Harvard Conference on Geometry and Statistics in 2023. He also co-organized two Interaction of Statistics and Geometry (ISAG) conferences in Singapore, hosted by the Institute of Mathematical Sciences (IMS). Additionally, he initiated two symposiums in China at the Beijing Institute of Mathematical Sciences and Applications (BIMSA) and the Shanghai Institute for Mathematics and Interdisciplinary Sciences (SIMIS), scheduled for 2023-2025.

https://zhigang-yao.github.io/

Title: Manifold fitting

Abstract:

The field exploring the interaction between statistics and geometry has been expanding rapidly in both scope and influence. The idea of manifold fitting traces back to H. Whitney’s work in the early 1930s. A central question is: given a set of data, under what conditions can we find a smooth d-dimensional surface (or manifold) that approximates it well, and how can we quantify the quality of that fit in terms of distance and smoothness? In this talk, I will give an overview of the manifold fitting problem and highlight some recent insights and developments. The discussion will draw on recent work by Yao, Yau, and collaborators, as well as ongoing research.