The Department of Biostatistics and Computational Biology at the University of Rochester conducts teaching and research in statistical theory and methodology oriented toward the health sciences. Our unique graduate program is located within a School of Medicine environment and provides many opportunities for stimulating interaction with applied research.
The department interprets the term statistics very broadly, with specialization available in probability, statistical theory and analysis, biostatistics, and interdisciplinary areas of application. Department faculty participate fully in graduate teaching and individual attention is given to each student through intensive advising, extensive small seminars, and research collaboration. Students have opportunities for supervised teaching and statistical consulting experience. Prior to completing their degrees, most Ph.D. students have several publications underway based on research done in collaboration with faculty members in biostatistics/statistics and in various medical departments.
The program interprets the term 'statistics very broadly and permits specialization in probability, statistical theory and analysis, biostatistics, and interdisciplinary areas of application.
Course work in statistics is concentrated in three areas - probability, inference, and data analysis. Beginning students should expect to spend all of their first year, most of their second year, and some of their third year taking formal courses. The balance of time is spent on reading and research. Students entering with advanced training in statistics may transfer credits at the discretion of their advisor and in accordance with University policy.
In general, the PhD program requires a minimum of four years of study, with five years of study being more common (see Timeline for Degree Completion). Prior to completion of the PhD, most students have some publications underway, including some work related to their dissertation research, possibly other methodological work done in collaboration with other members of the faculty, and often some applied papers with scientific researchers in other fields.
The Bioinformatics and Computational Biology (BCB) concentration is designed to educate the next generation of biostatisticians with the knowledge required to address critical scientific and public health questions, and in particular, equip them with the skills necessary to both develop and use quantitative and computational methodologies and tools to manage, analyze, and integrate massive amounts of complex biomedical data.
Students learn core statistical methods and obtain training in data analysis methodologies and computational skills and techniques necessary for handling Big Data in the biomedical and public health sciences. In addition to this training in core methods, the program also places great emphasis on cross-training to prepare students to work as part of interdisciplinary teams that require expertise in statistical data science: 1) training students with quantitative/computational science backgrounds to enhance their understanding of biological questions and biological interpretation, and 2) training students with biomedical science backgrounds to proficiently use bioinformatics and computational methods and tools to address scientific questions.
