The Division of Biostatistics provides an academic home for the conduct of methodological research and graduate level teaching in biostatistics. Strong methodological expertise in biostatistics is essential both for the development of efficient study designs and for the development and application of state of the art techniques for data analysis in Population Health Science research.

PhD Training in Biostatistics

The Division of Biostatistics is pleased to solicit applications for the Ph.D. program in Population Health Sciences with an emphasis in Biostatistics. The biostatistics training in this PhD program is interdisciplinary, distinguished by its rigorous training and practical collaboration. In this program, students will develop essential expertise in theory and methods in biostatistics. Students will have opportunities to not only work with Biostatistics faculty in development of statistical theory and methods, but also collaborate with health systems researchers, translational epidemiologists, and clinical and basic science investigators across the University of Utah and around the globe. We aim to train the next generation of biostatisticians with skills needed to analyze complex biomedical data in the Big Data era, and academic leaders who are committed to improving patient and population-oriented care.

More information about the PhD Program

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Study Design & Biostatistics Center (SDBC)

Center for Clinical & Translational Science
SDBC is a team of over 40 biostatisticians, epidemiologists, statistical geneticists, health economists, psychometricians, and survey design researchers with a mission to advance high quality research at the University of Utah and affiliated institutions.

Biostatistics Division Faculty

Tom Greene

Tom Greene, PhD

Research Interests:

  • Methods for longitudinal data analysis
  • Clinical trial design
  • Modern causal inference
  • Joint analysis of longitudinal and time-to-event data
  • Evaluation of surrogate endpoints



Ben Haaland, PhD

Research Interests:

  • Meta-analytic approaches to comparative effectiveness
  • Experimental design and modeling techniques for non-parametric regression problems and machine learning



 Jincheng Shen, PhD

Research Interests:

  • Causal inference
  • Machine learning methods for clinical and genetics studies


Yue Zhang

Yue Zhang, PhD

Research Interests:

  • Development and Applications of Statistcal Methods in the Epidemiology and Clinical Trial
  • Clustered and Longitudinal Data Analysis
  • Multilevel and Joint Outcome
  • Modeling Techniques

Division Chief

Tom Greene, PhD