UT Southwestern Medical Center
Director, Translational Informatics Program
Associate Professor, Health Data Science & Biostatistics
Peter O'Donnell Jr. School of Public Health
Building the bridge between AI, scalable data infrastructure, and statistical rigor to transform health records and biobank resources into precision-health discoveries.
I joined UT Southwestern Medical Center in 2025 after a decade on the faculty at Vanderbilt University Medical Center, where I held joint appointments in Biostatistics and Biomedical Informatics and founded the Translational Bioinformatics & Biostatistics Laboratory (TBILab).
At UTSW, I direct the Translational Informatics Program in collaboration with the Department of Internal Medicine — an interdisciplinary hub that unites statisticians, clinicians, and data engineers to transform electronic health record and biobank resources into precision-health discoveries.
My work is evolving toward AI-driven translational informatics, with a focus on building robust data infrastructure and applying statistical rigor to ensure that AI-powered clinical insights are both scalable and trustworthy.
Where we're headed — integrating AI with rigorous statistical methods and scalable infrastructure to advance precision health.
Developing and evaluating AI agents — including large language models and multimodal systems — for clinical reasoning, decision support, and autonomous care management, grounded in statistical validation frameworks.
Designing cloud-scale analytics platforms and research analysis environments that make EHR, biobank, genomics, and wearable data accessible, interoperable, and ready for discovery — from institutional biobanks to national networks.
Bringing high-dimensional statistics, causal inference, and robust evaluation methodology to ensure that AI-driven clinical tools are safe, unbiased, and scientifically validated before deployment.
Leveraging large-scale EHR-linked genomic data for phenome-wide association studies, disease multimorbidity modeling, drug safety and effectiveness evaluation, and clonal hematopoiesis research.
Our work sits at the intersection of several transformative initiatives at UT Southwestern.
A collaborative program with the Department of Internal Medicine that serves as an interdisciplinary hub — uniting clinical informaticians, biostatisticians, data engineers, and clinician-scientists to transform EHR and biobank resources into precision-health discoveries. A home for informaticians and data scientists who want to make a direct impact on patient care.
A New ChapterA landmark institutional initiative performing whole-exome sequencing on 150,000+ patients across UT Southwestern's Health System. SPARC creates one of the nation's largest academic biobanks, accelerating genetic discovery and the return of actionable results to clinical care. We contribute to the research analytics platform and computational genomics infrastructure.
Institutional BiobankHome to 26 faculty members spanning AI and machine learning, biostatistics and study design, computational biology, and clinical informatics. The department has built robust data infrastructures including disease-specific data commons integrating clinical, imaging, and molecular data across cancers, liver diseases, and neurodegenerative disorders.
HDSB at OSPH →UT Southwestern's newest school, established with a landmark $100 million gift — the largest to any public university school of public health in U.S. history. Dedicated to disease prevention, health disparities, and translating scientific discoveries into population-level health solutions. Connected to nearly 3,000 faculty, six Nobel Laureates, and over $500 million in annual research funding.
O'Donnell School of Public Health →Software we have built and continue to maintain for the research community.
Multi-Institutional Multimorbidity Explorer. Interactive visualization of disease co-occurrence patterns across large EHR-linked biobanks.
Explore →R package for detecting and visualizing association subgraphs in complex networks derived from biomedical data.
GitHub →Additional tools for PheWAS analysis, network modeling, multi-morbidity exploration, and reproducible bioinformatics workflows.
View all →We are building a team at UT Southwestern. If you are a data scientist, statistician, engineer, or clinician-scientist interested in the intersection of AI, data infrastructure, and translational health research, we'd like to hear from you.