MATRIX Lab
Multi-omic Analytics, Translational Research, Inference & eXplainable AI
We develop interpretable, causal, and AI-enabled methods for biomedical research, environmental health and beyond, building them into open-source software that other scientists can actually use.
From complex data to actionable biomedical insight.
Methods built for messy biological data
High-dimensional omics data are compositional, sparse, confounded, and observational. We build statistical and machine-learning methods that respect those realities, and we implement them on real studies.
Compositional data analysis
Bias-corrected differential abundance for microbiome and other relative-abundance data, with rigorous control of false discoveries.
Multi-omics integration
Joint modeling across metagenomics, metabolomics, proteomics, and clinical data to recover signals no single assay reveals.
Causal inference
Mediation, confounding, and treatment-effect estimation in observational omics studies, longitudinal cohorts, and clinical trials.
Explainable AI
Machine-learning models whose predictions come with an interpretable account of why, a prerequisite for biomedical adoption.
Environmental health
Linking exposures to the microbiome, the metabolome, and downstream health outcomes across the life course.
Open-source software
Every method is released with documented, maintained R or Python software, so that findings are reproducible by anyone.
Lab updates
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The MATRIX Lab website goes live.
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Two new awards begin: an MPower Early Scholars grant on late-onset asthma, and the Grand Challenges 2.0 team award The Air We Share.
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Invited talk on Predicting Mortality in Older Men Through Multi-Omics Integration at the ICSA Applied Statistics Symposium, following the same talk at ENAR in March.
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NIEHS funds a new R21 on the health impacts of wildfires and extreme heat among end-stage kidney disease patients, with Dr. Lin as contact PI.
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ANCOM-BC2 published in Nature Methods, extending bias-corrected differential abundance to multiple groups, covariates, and repeated measures.
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ANCOM-BC published in Nature Communications, now a standard method for microbiome differential abundance analysis.
Research support
Active and completed awards funding the lab's methodological and collaborative work.
TeachingCourses & workshops
Courses at UMD, invited talks at ENAR and JSM, and openly available materials.
MentoringStudent work
Mentee projects, student-led papers, theses, and where our alumni have gone.