Can Le

Can Le is an Associate Professor in the Department of Statistics at the University of California, Davis. He holds a Ph.D. in Statistics from the University of Michigan, Ann Arbor.

His research focuses on:

• Network Analysis: Developing statistical methods for community detection, network estimation, and graph sampling.
• Random Graph Theory: Studying concentration properties and regularization of random graphs.
• High-Dimensional Problems: Addressing inference challenges in complex data structures.

His publications (2011–2024) demonstrate expertise in spectral methods for networks, robust modeling of social effects, and adaptive estimation techniques. Recent work emphasizes perturbation-robust models and non-exchangeable network data.

He leads research supported by the NSF grant DMS-2015134 and collaborates with researchers on network-linked data problems.