With its rich history of innovation, the PhD program in the Department of Biostatistics based at the Harvard T. H. Chan School of Public Health provides an exceptional opportunity for students to join faculty in carrying on our tradition of addressing the greatest challenges in public health, biomedical research, and computational biology. The program is designed for those who have demonstrated both interest and ability in scholarly research, preparing students for careers in the theory and practice of biostatistics and bioinformatics while training them in the development of methodology, consulting, teaching, and collaboration on a broad spectrum of problems related to human health, genomics, and basic biology.
Students receive training in:
- Applying innovative probabilistic and statistical theory and computing methods to the development of new biostatistical or bioinformatics methodology, publishing original methodological research, and solving public health problems
- Providing scientific and biostatistical or bioinformatics leadership in the design, conduct, and analysis of collaborative research studies in medicine and public health
- Applying modern statistical and computational methods to effectively analyze complex medical and public health data, including the development of new software for nonstandard problems and simulation methods
- Collaborating and communicating effectively with research scientists in related disciplines
- Teaching biostatistics or bioinformatics effectively to health professionals, research scientists, and graduate students
Students join a community of leading scientists and educators from around the world. The department’s location in the heart of Boston’s Longwood Medical Area—home to Harvard Medical School, the Dana-Farber Cancer Institute, and many world-class hospitals—makes collaboration with eminent laboratory and clinical researchers a natural part of the educational experience.
The faculty includes leaders in the development of statistical methods for clinical trials and observational studies, studies on the environment, and genomics/genetics. Specific areas of statistical and bioinformatics research include traditional areas such as survival analysis, Bayesian statistics, meta-analysis, and spatial statistics, along with modern approaches to causal inference, personalized medicine, neurostatistics, big data, and computational biology. The department’s research in statistical methods and interdisciplinary collaborations provides many opportunities for student participation.
Students in the Biostatistics program are enrolled in and receive a PhD from the Graduate School of Arts and Sciences, even though they work primarily with Harvard T. H. Chan School of Public Health faculty.
All candidates for admission to the PhD program must have successfully completed calculus through multivariable integration and one semester of linear algebra and have knowledge of a programming language. The verbal, quantitative, and analytical aptitude tests of the Graduate Record Examination (GRE) General Test are recommended, but not required.
Candidates are also strongly encouraged to have:
- Completed courses in probability, statistics, advanced calculus or real analysis, and numerical analysis
- Practical knowledge of a statistical computing package such as SAS, Splus, R, Stata, or SPSS
- Completed courses in biology, computational biology, and genetics (if interested in bioinformatics)
- Knowledge of a scripting language such as Python or Perl and some familiarity with relational databases (if interested in bioinformatics)
From time to time the department will admit students to the program without this level of preparation with the understanding that the student will promptly make up any deficiencies, usually by taking additional courses prior to entering the program.
The doctoral program in biostatistics generally requires four to five years as a full-time student to complete, with the first few years devoted primarily to coursework and the latter two to three years focused on dissertation research, teaching, and collaborative research with faculty members.