Li Ma

Li Ma

Associate Professor of Statistical Science

External address: 
Box 90251, Durham, NC 27708-0251
Internal office address: 
217 Old Chemistry
(919) 684-2871


Research in high-dimensional inference, nonparametric methods, Bayesian modeling, and biostatistics. Tackling statistical and computational challenges in analyzing big data. A recent focus of my research is on using multi-scale techniques to construct flexible probability models that can be applied to massive data sets. Traditional nonparametric approaches, while enjoying many established theoretical properties, are often computationally intractable for big data. Multi-scale inference provides a general framework for tackling the computational bottleneck, while preserving the theoretical guarantees enjoyed by classical methods.

Go to my homepage for the most up-to-date information.

Education & Training

  • Ph.D., Stanford University 2011

  • A.B., University of Chicago 2006

  • M.S., University of Chicago 2006

Selected Grants

CAREER: Advances in multi-scale Bayesian inference and learning on massive data awarded by National Science Foundation (Principal Investigator). 2018 to 2023

ISBA 2020: 15th World Meeting of the International Society of Bayesian Analysis -- June 29-July 3, 2020 awarded by National Science Foundation (Principal Investigator). 2020

Effects of Aging and the Urinary Microbiome on Recurrent Urinary Tract Infections awarded by National Institutes of Health (Co Investigator). 2018 to 2020

Graphical multi-resolution scanning for cross-sample variation awarded by National Science Foundation (Principal Investigator). 2016 to 2020

Bioinformatics and Computational Biology Training Program awarded by National Institutes of Health (Mentor). 2005 to 2020

Bayesian recursive partitioning and inference on the structure of high-dimensional distributions awarded by National Science Foundation (Principal Investigator). 2013 to 2016

Soriano, J., and L. Ma. “Mixture modeling on related samples by ψ-stick breaking and kernel perturbation.” Bayesian Analysis, vol. 14, no. 1, Jan. 2019, pp. 161–80. Scopus, doi:10.1214/18-BA1106. Full Text

Mao, J., et al. “Bayesian Graphical Compositional Regression for Microbiome Data.” Journal of the American Statistical Association, Jan. 2019. Scopus, doi:10.1080/01621459.2019.1647212. Full Text

Christensen, J., and L. Ma. “A Bayesian hierarchical model for related densities by using Pólya trees.” Journal of the Royal Statistical Society. Series B: Statistical Methodology, Jan. 2019. Scopus, doi:10.1111/rssb.12346. Full Text