The program will admit a small number of exceptionally qualified students. Each student will be assigned to a member of the steering committee to plan and approve a course of study.
By the end of their second year, students will choose a thesis advisor from CAM and two additional thesis committee members. A student may propose an advisor who is not a member of CAM, with approval of the steering committee, in which case the additional members of the thesis committee will be from CAM.
The course requirements of the Ph.D. in Computational and Applied Mathematics are fairly low, consistent with the goal of involving students in original research early in their graduate careers. Together with an assigned advisor, students will select courses from core sequences and a diverse set of possible electives. Example topics include traditional areas such as partial differential equations, numerical analysis, and dynamical systems, as well as modern signal processing, machine learning, data collection and processing, optimization, stochastic modeling and analysis, and the statistical analysis of high dimensional data. Students will complete preliminary examinations in two chosen areas, typically in their second year of study. The track will be highly interdisciplinary, with many students interacting with at least one scientific domain.
Students are required to take nine quarter courses over the first two years, according to a plan designed in consultation with the advisor. This allows students to take preparatory courses as needed. Courses are chosen from a selected set of courses in computational, statistical, and mathematical foundations. Students are also required to take at least one graduate level course in a scientific domain such as chemistry, genetics, geophysical sciences, molecular biology, neuroscience, and physics. Examples of each group of courses is provided below.
As two example nine-course sequences, a student with a machine learning focus might take