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Education / Graduate Education / PhD Programs / Statistics / About the Program / Bioinformatics and Computational Biology


Bioinformatics and Computational Biology

Other Available Graduate Programs
PhD in Statistics(traditional)
Master of Arts in Statistics
MS in Medical Statistics

Program for the Degree of PhD in Statistics with Concentration in Bioinformatics and Computational Biology

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: 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.

The doctoral program in Statistics with BCB Concentration is administered by the School of Medicine and Dentistry, and hence School of Medicine and Dentistry regulations apply.

Entering PhD students need undergraduate preparation in mathematics, including advanced calculus or mathematical analysis, a course in linear and/or matrix algebra, and a year of probability and statistics. Basic courses in computer science and/or biology are also required. A course in statistical methods is also recommended. Normally, doctoral students are initially considered MA candidates; this non-thesis degree can be completed in three semesters or, in some cases, in one calendar year. PhD studies consist of additional specialized courses, seminars, lab rotations and supervised research leading to a dissertation. There is no foreign language requirement. Computer expertise is developed in the program.

Students are expected to spend a minimum of 40 months and a maximum of 66 months, not necessarily continuously, engaged in one or more of the following activities that enhances their education and skill sets as statisticians: teaching assistantship, research assistantship, participation on the statistical consulting rotation, and summer internships.

All MA/PhD students take a comprehensive (basic) examination at the beginning of the second year. PhD students take another written (advanced) examination at the beginning of the third year. Both examinations will cover material in the areas of probability, inference, data analysis, and bioinformatics and computational biology.

After beginning research on a dissertation topic, PhD students take an oral qualifying examination, consisting largely of a presentation of a thesis proposal to a faculty committee, the student's Thesis Committee. Upon completion of the dissertation, doctoral candidates present their work at a public lecture followed by an oral defense of the dissertation before the Thesis Committee.

Prior to completing degrees, 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. In general, the PhD program requires a minimum of four years of study, with five years of study being more common.

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. At the same time, students in the BCB concentration are expected to take both dry (computational) lab rotations, and, ideally, wet lab rotations for the first two years until selecting one or two thesis advisors (ideally one statistics advisor and one basic science advisor). The balance of time is spent on reading and research. Students entering with advanced training in statistics, bioinformatics, or computational biology may transfer credits at the discretion of their advisor and in accordance with University policy. A typical program for an entering student without previous training is as follows:

Year 1: Fall

  • BST 401 Probability Theory (4 credits)
  • BST 411 Statistical Inference (4 credits)
  • BST 464 Applied Linear Regression (4 credits)
  • BST 496 Wet/Dry Lab Rotation (1 credit)
  • BST 590 Supervised Teaching (2 credits)
  • IND 501 Ethics in Research (1 credit)

Year 1: Spring

  • BST 413 Bayesian Inference (4 credits)
  • BST 426 Linear Models (4 credits)
  • BST 466 Categorical Data Analysis (4 credits)
  • BST 496 Wet/Dry Lab Rotation (1 credit)
  • BST 590 Supervised Teaching (3 credits)

Year 2: Fall

  • BST 402 Stochastic Processes (4 credits)
  • BST 432 High Dimensional Data Analysis (4 credits)
  • BST 479 Generalized Linear Models (4 credits)
  • BST 496 Wet/Dry Lab Rotation (1 credit)
  • BST 590 Supervised Teaching (3 credits)

Year 2: Spring

  • BST 412 Large-Sample Theory and Methods (4 credits)
  • BST 433 Computational Systems Biology (4 credits)
  • BST 496 Wet/Dry Lab Rotation (1 credit)
  • BST 513 Analysis of Longitudinal and Dependent Data (4 credits)
  • BST 591 Reading Course at the PhD Level (3 credits)

Year 3+ Mostly reading and research, with some 400-level and 500-level courses.