Min Xu

Min Xu is an Assistant Professor in the Department of Statistics at Rutgers University – New Brunswick. He is affiliated with the School of Arts and Sciences and focuses his research on theoretical and methodological aspects of machine learning and high-dimensional statistics, with applications in network analysis and nonparametric estimation.

Education:

• Ph.D. in Machine Learning, Carnegie Mellon University (2015)
• B.S. in Electrical Engineering and Computer Science (with minor in Mathematics), UC Berkeley

Research Interests:

Min Xu’s research lies at the intersection of machine learning, high-dimensional statistics, and network science. He develops computationally scalable methods with strong theoretical guarantees for complex data structures, particularly in nonparametric estimation, network analysis, and large-scale inference.

His work addresses fundamental challenges in estimating high-dimensional distributions and understanding the structure of evolving networks, with applications in economics and social sciences.

Grants & Funding:

• NSF Grant DMS-2113671
• NSF Grant DMS-2311299

Research Trends:

Across his publications, a consistent theme is the development of statistically rigorous methods for high-dimensional and network data. His work spans optimal estimation in stochastic block models, convex M-estimation, and inference on dynamic network structures, with a strong emphasis on theoretical guarantees and practical scalability.

Affiliations:

Previously, Min Xu served as a departmental postdoctoral researcher in the Statistics Department at the Wharton School, University of Pennsylvania. He is currently based at Hill Center, Rutgers University.