Zhengchao Wan

Zhengchao Wan is an Assistant Professor in the Department of Mathematics at the University of Missouri, part of the College of Arts and Science. His research focuses on the intersection of machine learning, topological data analysis, metric geometry, and probability theory. He explores applications of these areas in graph-based learning, optimal transport, and geometric data analysis.

His work includes developing novel methods for graph neural networks, analyzing effective resistance in networks, and advancing topological techniques like persistent homology and the persistent Laplacian. He also contributes to foundational studies in metric geometry, including ultrametric spaces and Gromov-type distances.

Recent publications highlight his contributions to robust graph learning algorithms, flow matching ODE dynamics, and the theoretical underpinnings of graph transformers. His research bridges abstract mathematical concepts with practical machine learning challenges, emphasizing both theoretical rigor and real-world applicability.