20202021 Bulletin
Computational and Applied Mathematics, MS


The field of Computational and Applied Math is part of the broader discipline known as Computational Science and Engineering (CS&E) which aims to address important problems in all areas of science and engineering by mathematical modeling and scientific computing. Students in this master’s program learn both analytical and numerical methods of applied math and apply them in the context of mathematical modeling to problems arising from physics, biology, engineering, economics, finance, Earth and climate sciences, and the like. Students with undergraduate degrees in engineering, the sciences, economics, etc. can also be considered for admission to this program provided that they have completed multivariate/vector calculus, differential equations, linear algebra, and numerical methods (including programing languages, such as MATLAB) as part of their undergraduate studies.
Admission policies are detailed in the section of the Bulletin.
Degree Requirements
A minimum of 32 units of graduate coursework is required, including four core courses (16 units) and two restricted electives (8 units), as outlined below. Students who lack the prerequisite undergraduate coursework may be asked to complete more than 32 units. At least 20 units of coursework must be gamma courses–that is, 300level and above. A grade of B or above must be earned in gamma courses.
Satisfactory Academic Progress: The University’s policy on satisfactory academic progress applies. Students who do not maintain a minimum overall grade point average of 3.0 in Mathematics courses are placed on academic probation. Students who remain on academic probation after taking an additional 8 units of Mathematics courses may be excluded from the program.
Advising: Students are assigned an academic advisor upon entering the program.
Outline:
 Requires 8 graduate math courses (32 credits), at least 5 of which (20 credits) must be at the 300level or higher.
 Must include 4 of the following core courses:
 Math 306 Optimization
 Math 362 Numerical Methods for Differential Equations
 Math 368 Numerical Methods for Matrix Computations
 Math 387 Discrete Mathematical Modeling
 Math 388 Continuous Mathematical Modeling
 Math 462 Mathematics of Machine Learning
 2 restricted electives can be chosen from either the list of core courses, or from the following list (not all these courses are offered every year):
 Any of the core courses listed above if not taken to satisfy the core requirements
 Math 293393 Mathematics Clinic
 Math 256 Stochastic Processes
 Math 294 Methods of Applied Mathematics (waived if the student has had equivalent preparation: complex variables, vector calculus, Fourier series/transforms, and PDEs)
 Math 306 Optimization
 Math 354 Reliability Theory
 Math 357 Deterministic and Stochastic Control
 Math 358 Mathematical Finance
 Math 359 Computational Statistics
 Math 381 Fluid Dynamics
 Math 382 Perturbation and Asymptotic Analysis
 Math 384 Advanced Partial Differential Equations
 Math 385 Mathematical Modeling in Biology
 Math 386 Image Processing
 Math 389 Advanced Topics in Mathematics (if appropriate, with advisor’s approval)
 In lieu of one formal course, the student may take Math 398 Independent Study with a research advisor leading to a publication quality technical report in an area of computational or applied math.
Professional experience policy
In lieu of one formal course, students may take Math 398 Independent Study with a research advisor leading to a publication quality technical report in an area of the mathematical sciences. Subject to approval by their academic advisor, students working outside campus on mathematical/statistical projects may also use this professional experience as the basis of a Math 398 Independent Study. At most 2 units per semester can be acquired in this practical type of independent study, which will not be counted as a gamma course.
