The teaching and research programs of the Department of Applied Mathematics and Statistics span modern applied mathematics. The department's curriculum in Probability/Statistics covers probability theory, stochastic processes, and applied and theoretical statistics. Its Operations Research/Optimization program includes continuous and discrete optimization, numerical optimization, network programming, and game theory. Its curriculum in Discrete Mathematics includes combinatorics, graph theory and cryptology and coding. Its program in Scientific Computing includes computing, numerical analysis, matrix analysis, and mathematical modeling of systems. The programs of the department together emphasize mathematical reasoning, mathematical modeling and computation, abstraction from the particular, innovative application of mathematics, and development of new methodology.
The current University Catalog contains a detailed description of the department's courses, programs, and requirements and a list of the current faculty and their interests. The purpose of this handbook is to present supplemental information; it should be read along with the departmental listing in the Catalog. (In particular, the reader should note that the course offerings of this and other departments can change over the years in subject, frequency, and fall-or-spring timing, so that some of the sample programs listed below may require modification; see the current course list for the latest offerings.)
The objective of the department's Ph.D. program is to produce graduates who are broadly educated in Applied Mathematics and Statistics and who can work at the current research frontiers of their specialized disciplines. Follow this link for information about admission to the AM&S graduate program.
A student should demonstrate mathematical comprehension in two stages:
- general mathematical proficiency (breadth), measured by an Introductory Exam on the following topics: real analysis, linear algebra, and probability; and
- qualification for advance research (depth), measured by a Ph.D. Candidacy Exam covering in-depth knowledge of the student’s proposed research area.
A main objective is to help those students with the desire and background to do so, to get an earlier start on initial research-type activities. These activities would not necessarily be in the same area as the ultimate doctoral research, and typically would not have the same degree of intensity and commitment as that later work; for example they would involve interaction with the student’s faculty “mentor” rather than an official “dissertation advisor.” One role of the Introductory Exam is to gauge readiness for such activity.