The PhD curriculum in the Department of Computational Applied Mathematics and Operations Research at Rice University is designed to provide PhD students with a strong foundation, but also give them the flexibility to build a course portfolio focusing on their research area. The PhD curriculum is divided into introductory courses, distribution courses from Areas A, B, C specified below, and electives. PhD students develop their specific curriculum in consultation with their faculty advisor.
PhD students in the Department of Computational Applied Mathematics and Operations Research should complete the majority of the following introductory courses by the end of their first year and complete all introductory courses by the end of their second year.
- CMOR 500: Analysis I
- CMOR 520: Computational Science
- CMOR 524: Advanced Numerical Analysis I
- CMOR 530: Iterative Methods for Systems of Equations and Unconstrained Optimization
- CMOR 541: Linear and Integer Programming
In addition to the Introductory PhD Courses, CMOR PhD students are required to complete a minimum of one (1) course each from the three Areas A, B, C specified below, and one (1) elective 3-credit lecture course.
Area A — (Courses with emphasis on Foundations and Theory)
CMOR 505: Partial Differential Equations I
CMOR 501: Applied Functional Analysis
CMOR 527: Discontinuous Galerkin Methods
CMOR 526: Foundations of Finite Element Methods
CMOR 534: Intro to Partial Differential Equation Based Simulation and Optimization
CMOR 532: Optimization Theory
CMOR 504: Graph Theory
CMOR 552/STAT 581: Mathematical Probability I
CMOR 553/STAT 583: Introduction to Random Processes and Applications
500 level MATH courses with advisor approval
Area B — (Courses with emphasis on Algorithms and Computation)
CMOR 521: High Performance Computing
CMOR 523: Numerical Methods for PDEs
CMOR 527: Discontinuous Galerkin Methods for Solving Engineering Problems
CMOR 525: Numerical Linear Algebra
CMOR 533: Numerical Optimization
CMOR 531: Convex Optimization
CMOR 543: Combinatorial Optimization
CMOR 544: Stochastic Optimization
CMOR 551: Stochastic Simulation
CMOR 536: Optimization with Simulation Constraints
Area C — (Courses with emphasis on Modeling and Applications)
CMOR 510: Modeling Mathematical Physics
CMOR 555: Stochastic Control and Applications
CMOR 553: Introduction to Random Processes and Applications
CMOR 615: Theoretical Neuroscience 1: Biophysical Modeling of Cells and Circuits
STAT 502: Neural Machine Learning I
STAT 503: Topics in Methods and Data Analysis
STAT 514: Introduction to Biostatistics
STAT 519: Statistical Inference
500 level courses offered in the schools of Natural Sciences and Engineering with advisor approval
Course Descriptions and Other Requirements
Descriptions of all CMOR courses can be found in General Announcements.
The Department of Computational Applied Mathematics and Operations Research regularly reviews and refines its PhD curriculum and adjustments to the above courses and areas may be made. As always, these requirements are in addition to the general university requirements specified in the General Announcements.
The CMOR Graduate Handbook contains detailed information about exams, funding, required and recommended courses, and regulations and rules for the PhD degree program in the Department of Computational Applied Mathematics and operations Research.