Associate Professor
University of Wisconsin–Madison
Department of Biostatistics & Medical Informatics
Biostatistics · Biomedical Data Science
Better evidence for better health decisions.
I develop statistical and AI methods that make personalized health decisions more interpretable, equitable, and reliable.
Multiple openings · Fall 2026 / Spring 2027I welcome conversations about joining the group.
My group works where statistics, AI, and medicine meet—studying how to draw reliable causal conclusions, personalize care, and learn from complex biomedical data.
About
A statistician working across disciplines.
I am an Associate Professor in the Department of Biostatistics and Medical Informatics and an affiliate faculty member in the Department of Statistics at the University of Wisconsin–Madison. My research spans personalized treatment, causal inference, clinical AI, biomedical informatics, and microbiome science.
I collaborate with clinicians, computer scientists, statisticians, and population-health researchers to build methods that remain useful in the complexity of real-world care.
I currently serve as an Associate Editor for both theAnnals of Applied Statistics and Biometrics.
Department of StatisticsUW Carbone Cancer CenterHealth Innovation ProgramCenter for Health Disparities ResearchCenter for Demography of Health and AgingPsycheMERGE
Education & experience
The path to Madison.
Assistant Professor
University of Wisconsin–Madison
Department of Biostatistics & Medical Informatics
Assistant Professor
Vanderbilt University
Department of Biostatistics
PhD, Biostatistics
University of North Carolina at Chapel Hill
Dissertation recognized with the Margolin Award
BS, Bioinformatics
Huazhong University of Science and Technology
Wuhan, China
Research at a glance
Four connected research areas.
01
Causal inference
Generalizing evidence and learning fair, interpretable treatment strategies across populations.
02
Clinical AI
Building multimodal and language-based models that clinicians can inspect, trust, and use.
03
Microbiome & multi-omics
Connecting complex biological communities, host genetics, and health through robust statistics.
04
Precision medicine
Turning heterogeneous patient data into practical, individualized health decisions.
Multiple openings · Fall 2026 / Spring 2027
Prospective graduate students and postdoctoral researchers
If your interests connect with statistical learning, causal inference, clinical AI, or precision health, send a CV and a short note about the problems you hope to work on.
Start a conversation