Requirements & Curriculum Overview

MS Requirements:

Requirements include:

  1. Completion of two years/four semesters of study
  2. Completion of required coursework with an average of a High Pass*
    • Ten credits must be taken at Yale
    • This coursework includes the successful completion of three research rotations
  3. Satisfaction of the Graduate School requirement of at least two Honors* grades

*Graduate School Grading: Honors (H), High Pass (HP), Pass (P), Fail (F) plus Satisfactory/Unsatisfactory (Sat/Unsat)

A Master’s degree may be obtained by a CBB PhD student who is en route to obtaining a PhD degree or who leaves Yale prior to receiving a PhD degree.

PhD Requirements:

This section outlines the current CBB curriculum, and other requirements for the PhD degree. Because of the interdisciplinary nature of the field, we anticipate that the students will be extremely heterogeneous in their background and training. As a result, the co-directors are willing to meet with students to help them individually tailor the curriculum to their background and interests. The emphasis will be on gaining competency in three broad “core areas”:

  • Computational biology and bioinformatics
  • Biological sciences (e.g., genetics, immunobiology, cell biology, etc.)
  • Informatics (e.g., computer science, statistics, applied mathematics, etc.)

Completion of the curriculum will typically take 4 semesters, depending in part on the prior training of the student. Since students may have very different prior training in biology and computing, the courses taken may vary considerably. In addition, students will spend a significant amount of time during this period doing intensive research rotations in faculty laboratories and attending relevant lectures and seminars.

Specifically, we expect that all students will:

  • Complete at least ten credits through specific courses as follows:
    • Three required graduate courses in computational biology and bioinformatics
    • Two graduate courses in the biological sciences
    • Two graduate courses in areas of informatics
    • Two additional courses in any of the three core areas (which may be undergraduate courses with approval)
    • One year-long graduate course that consists of three lab rotations taken over the fall and spring semesters of the first year (graded as Pass or Fail)
    • Any additional courses required to satisfy areas of minimum expected competency
  • Take a half-semester graduate seminar on research ethics in the 1st and 4th years (graded as credit or non-credit)
  • Participate in the CBB seminar series
  • Serve as a teaching assistant in two semester courses

Students will typically take 2-3 courses each semester and 3 research rotations during the first year. Students are expected to find a dissertation adviser (or co-advisers) by the end of the first year. In the summer after the first year, students will start working in the laboratory of their chosen PhD supervisor. Students must pass a qualifying examination normally given at the end of the second year or the beginning of the third year. There is no language requirement.

Students may be able to waive some course requirements based on graduate coursework completed at other universities where they have been enrolled as a graduate student. Courses must be equivalent to Yale graduate courses, and the Graduate School usually sets a maximum limit of three courses that can be waived.

In addition to the curriculum outlined above, the program has also defined an initial set of guidelines for minimum expected competency in biology, computer science, statistics, and mathematics. Some students may have satisfied all of these areas prior to entering our program. Other students may need to take undergraduate or graduate courses at Yale to satisfy one or more of these specific areas. These guidelines are in evolution and may be refined over time as we get more experience with the program.

  • Biological Sciences
    • Introductory biology
    • Biochemistry
  • Informatics
    • Data structures and programming techniques
    • Introduction to probability and statistical inference
    • Multivariate calculus and linear algebra