PhD in Statistics - Bioinformatics and Computational Biology in Rochester United States | University of Rochester

The Department of Biostatistics and Computational Biology at the University of Rochester focuses on teaching and research in statistical theory and methods applied to health sciences. Our distinctive graduate program, situated within a School of Medicine setting, fosters dynamic collaborations with applied research initiatives.
The department embraces a wide-ranging definition of statistics, offering specializations in probability, statistical theory, biostatistics, and interdisciplinary applications. Faculty members are deeply involved in graduate education, providing personalized mentorship through close advising, small seminar discussions, and joint research projects. Students gain hands-on experience through teaching assistantships and statistical consulting roles. Many PhD candidates begin publishing research papers during their studies, often co-authored with faculty from biostatistics and medical departments.
Our program adopts an expansive view of statistics, allowing students to specialize in probability, statistical theory, biostatistics, or interdisciplinary applications.
The curriculum emphasizes three core areas: probability, statistical inference, and data analysis. First-year students typically devote their full schedule to coursework, with significant course loads continuing through the second year and into the third year. Remaining time focuses on independent study and research. Students with prior statistical training may receive credit transfers following advisor approval and university guidelines.
Typically, the PhD program spans four to five years (refer to Degree Completion Timeline). Most candidates initiate multiple publications before graduating, including dissertation-related work, collaborative methodological projects with faculty, and often applied research with scientists from other disciplines.
The Bioinformatics and Computational Biology (BCB) specialization prepares future biostatisticians to tackle pressing scientific and public health challenges, providing advanced training in computational approaches for managing and interpreting large-scale biomedical datasets.
Students master fundamental statistical techniques while developing computational proficiency for handling biomedical Big Data. The program strongly emphasizes interdisciplinary preparation, equipping students to collaborate effectively in statistical data science teams: 1) enhancing quantitative researchers' biological understanding, and 2) enabling biomedical scientists to competently apply bioinformatics tools to research questions.