Min Xu
Assistant Professor, Department of Statistics
Rutgers University - New Brunswick
Email: mx76 [AT] stat [DOT] rutgers [DOT] edu
Short Bio
I was previously the departmental postdoctoral researcher in the Statistics Department at the Wharton School of the University of Pennsylvania. I received my Ph.D. in Machine Learning in 2015 at Carnegie Mellon University under the supervision of Prof. John Lafferty. I have B.S. in electrical engineering and computer science with a minor in mathematics from UC Berkeley. My research studies emerging estimation problems in the analysis of large and complex data and proposes methods that have both scalable computation and strong theoretical guarantees. I am particularly interested in high-dimensional statistics, network data analysis, and machine learning problems with applications in economics.
My research is supported by NSF grant DMS-2113671 and DMS-2311299.
Research Interests
Network data analysis | Nonparametric statistical methods | High-dimensional statistics | Machine learning
Select Publications
Optimal convex M-estimation via score matching
Oliver Feng, Yu-Chun Kao, Min Xu, Richard Samworth (2024) [arXiv]
Root and community inference on the latent growth process of a network
Harry Crane and Min Xu. (2023)
To appear, Journal of the Royal Statistical Society, Series B (with discussion).
[arXiv]
High-dimensional nonparametric density estimation via shape and symmetry constraints
Min Xu and Richard J. Samworth. (2021)
Annals of Statistics.
[Paper] [Supplementary Materials]Optimal rates for community estimation on the weighted stochastic block model
Min Xu, Varun Jog, and Po-Ling Loh (2020)
Annals of Statistics.
[Paper and Supplementary Materials]