Doctor of Philosophy in Biostatistics

The PhD in biostatistics is an academic degree program for students with a background in mathematics and a strong interest in biology and public health. The program emphasizes statistical theory and methods so that students are prepared to be effective statistical collaborators in interdisciplinary studies; lead the design and execution of studies; and develop biostatistics methodology.


Recent graduates hold the following positions:

  • Data scientist, Google 
  • Senior research statistician, AbbVie Inc.
  • Biostatistics manager, Amgen
  • Senior biostatistician, Boehringer Ingelheim
  • Senior research investigator, Bristol-Myers Squibb
  • Biostatistician, Duke Clinical Research Institute
  • Aassistant professor, Medical College of Wisconsin
  • Assistant professor, Northwestern University Feinberg School of Medicine
  • Assistant professor, University of Florida
  • Postdoctoral associate, University of Pittsburgh
  • Mathematical statistician, U.S. Food and Drug Administration

Program Information

PhD Degree Requirements Worksheet (PDF, 2023-24)
Student Handbook (PDF, 2023-24)


Statistical Genetics

Doctoral students interested in statistical genetics can pursue that training through either the biostatistics PhD program or the human genetics PhD program. Within the biostatistics PhD program, statistical genetics students take the usual requirements for a biostatistical major but their electives are appropriately selected genetics courses. Students interested in statistical genetics should state that in their application.

A partial list of faculty with interest in statistical genetics

Department of Biostatistics
Yong Seok Park
Chien-Cheng (George) Tseng

Department of Human Genetics with secondary appointment in the Department of Biostatistics
Daniel E. Weeks
Eleanor Feingold


Graduates will be able to:

  • Develop and implement advanced parametric and nonparametric methods, and the corresponding inference procedures
  • Formulate various linear and mixed models and master the statistical inference on these models
  • Apply linear, generalized linear and non-linear regression models to analyze cross-sectional or clustered, or longitudinal data with applications to health sciences
  • Derive quantities and inference statistics for time-to-event data and apply nonparametric, parametric and semiparametric survival models to such data
  • Contribute to the body of knowledge in the field of biostatistics by submitting article(s) for publication in peer-reviewed journal(s), or preparing book chapter(s) for publication