Health Data Science and Biostatistics
We use data and statistical methods to improve health research, policy, and care. Our department works across disciplines to develop innovative tools and train future leaders in health data science and biostatistics.
Our Vision
The Department of Health Data Sciences and Biostatistics focuses on leveraging data to improve public health outcomes. We develop and apply advanced statistical and computational methods to address challenges in medicine, epidemiology, and health policy. Our faculty collaborate across disciplines to drive impactful research, while our academic programs prepare students to become skilled professionals in health data science and biostatistics. Committed to innovation, education, and collaboration, we aim to transform health care through data-driven insights.
Research Interests
The field of Health Data Sciences and Biostatistics focuses on the application of advanced statistical methods and data analytics to address pressing challenges in healthcare and public health. Our research explores innovative approaches to health data analysis, predictive modeling, and evidence-based decision-making, with the goal of improving patient outcomes, enhancing public health policies, and advancing precision medicine.
Education & Training
The Department of Health Data Sciences and Biostatistics trains graduate students and postdoctoral fellows and has new positions and opportunities available each year. Find out more about our broad- based program at OSPH Admissons (https://osph.utsouthwestern.edu/admissions/)
Who We Are
Yang Xie, Ph.D.
Professor & Interim Chair
Dr. Yang Xie holds the Raymond D. and Patsy R. Nasher Distinguished Chair in Cancer Research and is the Associate Dean for Data Sciences at UT Southwestern Medical Center. She is the founding director of the Quantitative Biomedical Research Center (QBRC), the Pediatric Cancer Data Commons (PCDC), and the Cancer Center Data Science Shared Resources (DSSR) at the Harold C. Simmons Comprehensive Cancer Center. Dr. Yang Xie received her training in biostatistics, medicine and epidemiology. Her research lab focuses on medical informatics, developing predictive and prognostic biomarkers, and precision medicine. She is currently the PI of an NIH Maximizing Investigators' Research Award (MIRA) grant, MPI of an NIAID U01 grant and PI of the Pediatric Cancer Data Core at CPRIT.
Xie Lab
Our lab focuses on improving treatments for cancer by applying computer science and statistical methodologies to analyzing high-throughput biological data. New models and tools are currently in development to assist the investigation of disease mechanisms and their related diagnostic innovations.
Song Zhang, Ph.D.
Professor, Interim Vice Chair
Dr. Zhang joined UT Southwestern as an assistant professor in September 2007. He currently serves as the director of BERD (Biostatistics, Epidemiology, and Research Design) for the UTSW CTSA program. He also serves on the NCI Central Institutional Review Board (Adult CIRB – Early Phase Emphasis). Dr. Zhang’s research interest in statistical methodology lies in two main areas: Bayesian hierarchical modeling and clinical trial design. He has published multiple papers on the application of Bayesian hierarchical models to disease mapping, joint modeling of longitudinal and survival outcomes, item- response theory for grant review, functional enrichment analysis to detect important pathways, and multi-level modeling to detect factors that impact cancer screening, etc. Another area of his research interest is design methodology for clinical trials to account for various pragmatic issues such as correlated outcomes (clustered and longitudinal), missing data, small sample sizes, historical control, random variability in cluster size, and cost constraints, etc. He has published multiple high quality papers in this area and in 2015 he co-authored a book titled “Sample Size Calculations on Clustered and Longitudinal Outcomes in Clinical Research” (Chapman & Hall, New York). Dr. Zhang has been successful in securing extramural grants as the PI to support his independent research program, examples include an NIH R03 grant to conduct secondary data analysis on VA HIV registry; an NSF grant to build risk prediction model based on electronic health record data; and a PCORI methodology development grant to address pragmatic design issues in stepped-wedge cluster randomized trials.
View our academic programs and faculty pages.