Li Ma | Scholars@Duke

Li Ma
Associate Professor of Statistical Science

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.

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Current Appointments & Affiliations

Associate Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2018
Associate Professor of Biostatistics and Bioinformatics, Biostatistics & Bioinformatics, Basic Science Departments 2020

Contact Information

217 Old Chemistry Bldg, Box 90251, Durham, NC 27708-0251
Box 90251, Durham, NC 27708-0251
li.ma@duke.edu (919) 684-2871
Personal site

Background
Education, Training, & Certifications
- Ph.D., Stanford University 2011
- A.B., The University of Chicago 2006
- M.S., The University of Chicago 2006
Previous Appointments & Affiliations
- Assistant Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2011 - 2018
Recognition
Awards & Honors
Expertise
Subject Headings
Research
Selected Grants
- NSF Engineering Research Center for Precision Microbiome Engineering (PreMiEr) awarded by National Science Foundation 2022 - 2027
- Statistical modeling of cross-sample variation and learning of latent structures in microbiome sequencing data awarded by National Institutes of Health 2020 - 2025
- Collaborative: Bayesian Residual Learning and Random Recursive Partitioning Methods for Gaussian Processes Modeling awarded by National Science Foundation 2022 - 2025
- CAREER: Advances in multi-scale Bayesian inference and learning on massive data awarded by National Science Foundation 2018 - 2023
- Advances in Bayesian nonparametric methods for jointly modeling multiple data sets awarded by National Science Foundation 2020 - 2023
- Urinary Lactobacilli and microbial interference in the aging urinary microbiome awarded by American Association of Obstetricians and Gynecologists Foundation 2020 - 2022
- ISBA 2020: 15th World Meeting of the International Society of Bayesian Analysis -- June 29-July 3, 2020 awarded by National Science Foundation 2020 - 2021
- Bioinformatics and Computational Biology Training Program awarded by National Institutes of Health 2005 - 2021
- Effects of Aging and the Urinary Microbiome on Recurrent Urinary Tract Infections awarded by National Institutes of Health 2018 - 2021
- Graphical multi-resolution scanning for cross-sample variation awarded by National Science Foundation 2016 - 2020
- Bayesian recursive partitioning and inference on the structure of high-dimensional distributions awarded by National Science Foundation 2013 - 2016
External Relationships
- Ministry of University Research of Italy
- Universita Bocconi
Publications & Artistic Works
Selected Publications
- Academic Articles
  - Ji, Zhicheng, and Li Ma. “Controlling taxa abundance improves metatranscriptomics differential analysis.” Bmc Microbiol 23, no. 1 (March 7, 2023): 60. https://doi.org/10.1186/s12866-023-02799-9.
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  - Li, Meng, and Li Ma. “Learning Asymmetric and Local Features in Multi-Dimensional Data Through Wavelets With Recursive Partitioning.” Ieee Transactions on Pattern Analysis and Machine Intelligence 44, no. 11 (November 2022): 7674–87. https://doi.org/10.1109/tpami.2021.3110403.
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  - LeBlanc, Patrick, and Li Ma. “Microbiome subcommunity learning with logistic-tree normal latent Dirichlet allocation.” Biometrics, October 2022. https://doi.org/10.1111/biom.13772.
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  - Gorsky, S., and L. Ma. “Multi-scale Fisher's independence test for multivariate dependence.” Biometrika 109, no. 3 (September 2022): 569–87. https://doi.org/10.1093/biomet/asac013.
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  - Gorsky, S., and L. Ma. “Rejoinder: 'Multi-scale Fisher's independence test for multivariate dependence'.” Biometrika 109, no. 3 (September 1, 2022): 605–9. https://doi.org/10.1093/biomet/asac034.
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  - Mao, Jialiang, and L. I. Ma. “DIRICHLET-TREE MULTINOMIAL MIXTURES FOR CLUSTERING MICROBIOME COMPOSITIONS.” The Annals of Applied Statistics 16, no. 3 (September 2022): 1476–99. https://doi.org/10.1214/21-aoas1552.
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  - Vaughan, Monique H., Gregory E. Zemtsov, Erin M. Dahl, Lisa Karstens, Li Ma, and Nazema Y. Siddiqui. “Concordance of urinary microbiota detected by 16S ribosomal RNA amplicon sequencing vs expanded quantitative urine culture.” Am J Obstet Gynecol, June 25, 2022. https://doi.org/10.1016/j.ajog.2022.06.031.
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  - Luo, Kaixuan, Jianling Zhong, Alexias Safi, Linda K. Hong, Alok K. Tewari, Lingyun Song, Timothy E. Reddy, Li Ma, Gregory E. Crawford, and Alexander J. Hartemink. “Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data.” Genome Res 32, no. 6 (June 2022): 1183–98. https://doi.org/10.1101/gr.272203.120.
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  - Awaya, N., and L. Ma. “Hidden Markov Pólya Trees for High-Dimensional Distributions.” Journal of the American Statistical Association, January 1, 2022. https://doi.org/10.1080/01621459.2022.2105223.
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  - Siddiqui, Nazema Y., Li Ma, Linda Brubaker, Jialiang Mao, Carter Hoffman, Erin M. Dahl, Zhuoqun Wang, and Lisa Karstens. “Updating Urinary Microbiome Analyses to Enhance Biologic Interpretation.” Front Cell Infect Microbiol 12 (2022): 789439. https://doi.org/10.3389/fcimb.2022.789439.
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  - Vaughan, Monique H., Jialiang Mao, Lisa A. Karstens, Li Ma, Cindy L. Amundsen, Kenneth E. Schmader, and Nazema Y. Siddiqui. “The Urinary Microbiome in Postmenopausal Women with Recurrent Urinary Tract Infections.” J Urol 206, no. 5 (November 2021): 1222–31. https://doi.org/10.1097/JU.0000000000001940.
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  - Giri, Vinay K., Kristin G. Kegerreis, Yi Ren, Lauren M. Bohannon, Erica Lobaugh-Jin, Julia A. Messina, Anita Matthews, et al. “Chlorhexidine Gluconate Bathing Reduces the Incidence of Bloodstream Infections in Adults Undergoing Inpatient Hematopoietic Cell Transplantation.” Transplant Cell Ther 27, no. 3 (March 2021): 262.e1-262.e11. https://doi.org/10.1016/j.jtct.2021.01.004.
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  - Ramalingam, Sendhilnathan, Sharareh Siamakpour-Reihani, Lauren Bohannan, Yi Ren, Alexander Sibley, Jeff Sheng, Li Ma, et al. “A phase 2 trial of the somatostatin analog pasireotide to prevent GI toxicity and acute GVHD in allogeneic hematopoietic stem cell transplant.” Plos One 16, no. 6 (January 2021): e0252995. https://doi.org/10.1371/journal.pone.0252995.
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  - Mao, J., Y. Chen, and L. Ma. “Bayesian Graphical Compositional Regression for Microbiome Data.” Journal of the American Statistical Association 115, no. 530 (April 2, 2020): 610–24. https://doi.org/10.1080/01621459.2019.1647212.
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  - 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 82, no. 1 (February 1, 2020): 127–53. https://doi.org/10.1111/rssb.12346.
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  - Ma, L., and J. Mao. “Fisher Exact Scanning for Dependency.” Journal of the American Statistical Association 114, no. 525 (January 2, 2019): 245–58. https://doi.org/10.1080/01621459.2017.1397522.
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  - Soriano, J., and L. Ma. “Mixture modeling on related samples by ψ-stick breaking and kernel perturbation.” Bayesian Analysis 14, no. 1 (January 1, 2019): 161–80. https://doi.org/10.1214/18-BA1106.
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  - Ma, L., and J. Soriano. “Analysis of Distributional Variation Through Graphical Multi-Scale Beta-Binomial Models.” Journal of Computational and Graphical Statistics 27, no. 3 (July 3, 2018): 529–41. https://doi.org/10.1080/10618600.2017.1402774.
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  - Tang, Y., L. Ma, and D. L. Nicolaes. “A phylogenetic scan test on a dirichlet-tree multinomial model for microbiome data.” Annals of Applied Statistics 12, no. 1 (March 1, 2018): 1–26. https://doi.org/10.1214/17-AOAS1086.
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  - Soriano, J., and L. Ma. “Probabilistic multi-resolution scanning for two-sample differences.” Journal of the Royal Statistical Society. Series B: Statistical Methodology 79, no. 2 (March 1, 2017): 547–72. https://doi.org/10.1111/rssb.12180.
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  - Ma, L. “Recursive partitioning and multi-scale modeling on conditional densities.” Electronic Journal of Statistics 11, no. 1 (2017): 1297–1325. https://doi.org/10.1214/17-EJS1254.
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  - Ma, L., and J. Soriano. “Efficient functional ANOVA through wavelet-domain Markov groves.” Journal of the American Statistical Association, 2017.
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  - Ma, L. “Adaptive Shrinkage in Pólya Tree Type Models.” Bayesian Analysis 12, no. 3 (September 2016). https://doi.org/10.1214/16-BA1021.
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  - Ma, L. “Scalable Bayesian Model Averaging Through Local Information Propagation.” Journal of the American Statistical Association 110, no. 510 (April 3, 2015): 795–809. https://doi.org/10.1080/01621459.2014.980908.
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  - Ma, L. “Adaptive testing of conditional association through recursive mixture modeling.” Journal of the American Statistical Association 108, no. 504 (January 1, 2013): 1493–1505. https://doi.org/10.1080/01621459.2013.838899.
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  - Ma, Li, Wing Hung Wong, and Art B. Owen. “A sparse transmission disequilibrium test for haplotypes based on Bradley-Terry graphs.” Human Heredity 73, no. 1 (January 2012): 52–61. https://doi.org/10.1159/000335937.
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  - Ma, L., and W. H. Wong. “Coupling optional pólya trees and the two sample problem.” Journal of the American Statistical Association 106, no. 496 (December 1, 2011): 1553–65. https://doi.org/10.1198/jasa.2011.tm10003.
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  - Ma, L., M. L. Stein, M. Wang, A. O. Shelton, C. A. Pfister, and K. J. Wilder. “A method for unbiased estimation of population abundance along curvy margins.” Environmetrics 22, no. 3 (May 1, 2011): 330–39. https://doi.org/10.1002/env.1053.
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  - Ma, L., D. Mease, and D. M. Russell. “A four group cross-over design for measuring irreversible treatments on web search tasks.” Proceedings of the Annual Hawaii International Conference on System Sciences, March 28, 2011. https://doi.org/10.1109/HICSS.2011.11.
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  - Ma, Li, Themistocles L. Assimes, Narges B. Asadi, Carlos Iribarren, Thomas Quertermous, and Wing H. Wong. “An "almost exhaustive" search-based sequential permutation method for detecting epistasis in disease association studies.” Genetic Epidemiology 34, no. 5 (July 2010): 434–43. https://doi.org/10.1002/gepi.20496.
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  - Wong, W. H., and L. Ma. “Optional Pólya tree and Bayesian inference.” Annals of Statistics 38, no. 3 (June 1, 2010): 1433–59. https://doi.org/10.1214/09-AOS755.
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- Conference Papers
  - Liu, R., M. Li, and L. Ma. “CARP: Compression through Adaptive Recursive Partitioning for Multi-Dimensional Images.” In Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition, 14294–302, 2020. https://doi.org/10.1109/CVPR42600.2020.01431.
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Teaching & Mentoring
Recent Courses

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Education, Training, & Certifications

Previous Appointments & Affiliations

Awards & Honors

Subject Headings

Selected Grants

External Relationships

Selected Publications

Academic Articles

Conference Papers

Recent Courses