Nan Wu

Nan Wu is an Assistant Professor in the Department of Mathematical Sciences at the University of Texas at Dallas (UTD). He holds a Ph.D. in Mathematics from the University of Toronto (2018) and a B.Sc. in Mathematics and Physics from the same institution (2011). His research focuses on manifold learning, exploring geometric structures of high-dimensional data and their applications in nonparametric statistics and stochastic dynamics. Key areas include nonlinear dimension reduction, data denoising, and algorithm development for manifold-based analysis.

Education

• Ph.D. in Mathematics, University of Toronto, 2018
• B.Sc. in Mathematics and Physics, University of Toronto, 2011

Research Interests

Wu’s work bridges differential geometry and machine learning, emphasizing manifold learning techniques. He develops mathematical foundations for nonlinear dimension reduction algorithms (e.g., Locally Linear Embedding, Diffusion Maps) and applies these to problems in statistics, such as kernel density estimation and efficient solvers for Langevin dynamics. His geometric methods address challenges in data denoising, boundary detection, and manifold reconstruction from noisy observations.

Teaching

He has taught courses in probability (UTD), multivariable calculus (Duke University), and introductory calculus (University of Toronto).

Publications Overview

His publications span topics like spectral convergence of graph Laplacians, Gaussian process modeling on manifolds, and geometric analysis of minimal surfaces. Recent work includes boundary detection algorithms and adaptive Bayesian methods for data with low intrinsic dimensionality.