Li Ma

Assistant Professor of Statistical Science

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

Overview

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.

Education & Training

  • Ph.D., Stanford University 2011

  • M.S., University of Chicago 2006

  • A.B., University of Chicago 2006

Bioinformatics and Computational Biology Training Program awarded by National Institutes of Health (Mentor). 2005 to 2021

Graphical multi-resolution scanning for cross-sample variation awarded by National Science Foundation (Principal Investigator). 2016 to 2019

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 Ma, L. "Probabilistic multi-resolution scanning for two-sample differences." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 79.2 (March 2017): 547-572. Full Text

Ma, L, and Soriano, J. "Efficient functional ANOVA through wavelet-domain Markov groves (Published online)." Journal of the American Statistical Association (2017).

Ma, L. "Scalable Bayesian Model Averaging Through Local Information Propagation." Journal of the American Statistical Association 110.510 (April 3, 2015): 795-809. Full Text

Ma, L. "Adaptive Testing of Conditional Association Through Recursive Mixture Modeling." Journal of the American Statistical Association 108.504 (December 2013): 1493-1505. Full Text

Ma, L, and Wong, WH. "Coupling optional pólya trees and the two sample problem." Journal of the American Statistical Association 106.496 (2011): 1553-1565. Full Text

Ma, L, Mease, D, and Russell, DM. "A four group cross-over design for measuring irreversible treatments on web search tasks." Proceedings of the Annual Hawaii International Conference on System Sciences (2011). Full Text

Ma, L, Stein, ML, Wang, M, Shelton, AO, Pfister, CA, and Wilder, KJ. "A method for unbiased estimation of population abundance along curvy margins." Environmetrics 22.3 (2011): 330-339. Full Text

Ma, L, Assimes, TL, Asadi, NB, Iribarren, C, Quertermous, T, and Wong, WH. "An "almost exhaustive" search-based sequential permutation method for detecting epistasis in disease association studies." Genetic Epidemiology 34.5 (2010): 434-443. Full Text

Pages