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Li Ma

Professor of Statistical Science
Statistical Science
Box 90251, Durham, NC 27708-0251
217 Old Chemistry Bldg, Box 90251, Durham, NC 27708-0251

Overview


Recent research interests are in generative models, tree-based models and methods, nonparametric methods, high-dimensional inference, scalable inference, Bayesian modeling, statistical computation and applied statistics. Tackling statistical and computational challenges in analyzing big data. Go to my website at lm186.github.io for the most up-to-date information.

Current Appointments & Affiliations


Professor of Statistical Science · 2023 - Present Statistical Science, Trinity College of Arts & Sciences
Professor in Biostatistics and Bioinformatics · 2023 - Present Biostatistics & Bioinformatics, Basic Science Departments

Recent Publications


Efficient in-situ image and video compression through probabilistic image representation

Journal Article Signal Processing · February 1, 2024 Fast and effective image compression for multi-dimensional images has become increasingly important for efficient storage and transfer of massive amounts of high-resolution images and videos. In this paper, we present an efficient in-situ method for multi- ... Full text Cite

Hidden Markov Pólya Trees for High-Dimensional Distributions

Journal Article Journal of the American Statistical Association · January 1, 2024 The Pólya tree (PT) process is a general-purpose Bayesian nonparametric model that has found wide application in a range of inference problems. It has a simple analytic form and the posterior computation boils down to beta-binomial conjugate updates along ... Full text Cite

Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses

Journal Article Bayesian Analysis · January 1, 2024 Cytometry is the standard multi-parameter assay for measuring single cell phenotype and functionality. It is commonly used for quantifying the relative frequencies of cell subsets in blood and disaggregated tissues. A typical analysis of cytometry data inv ... Full text Cite
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Recent Grants


NSF Engineering Research Center for Precision Microbiome Engineering (PreMiEr)

ResearchInvestigator · Awarded by National Science Foundation · 2022 - 2027

Collaborative: Bayesian Residual Learning and Random Recursive Partitioning Methods for Gaussian Processes Modeling

ResearchPrincipal Investigator · Awarded by National Science Foundation · 2022 - 2026

Statistical modeling of cross-sample variation and learning of latent structures in microbiome sequencing data

ResearchPrincipal Investigator · Awarded by National Institute of General Medical Sciences · 2020 - 2025

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Education, Training & Certifications


Stanford University · 2011 Ph.D.
The University of Chicago · 2006 A.B.
The University of Chicago · 2006 M.S.

External Links


Personal Site