Research in Applied Mathematics

Faculty and students interested in the applications of mathematics are an integral part of the Department of Mathematics; there is no formal separation between pure and applied mathematics, and the Department takes pride in the many ways in which they enrich each other. We also benefit tremendously from close collaborations with faculty and students in other departments at UC Berkeley as well as scientists at Lawrence Berkeley National Laboratory and visitors to the Mathematical Sciences Research Institute.

The Department regularly offers courses in ordinary and partial differential equations and their numerical solution, discrete applied mathematics, the methods of mathematical physics, mathematical biology, the mathematical aspects of fluid and solid mechanics, approximation theory, scientific computing, numerical linear algebra, and mathematical aspects of computer science. Courses in probability theory, stochastic processes, data analysis and bioinformatics are offered by the Department of Statistics, while courses in combinatorial and convex optimization are offered by the Department of Industrial Engineering and Operations Research. Our students are encouraged to take courses of mathematical interest in these and many other departments.

Topics explored intensively by our faculty and students in recent years include scientific computation and the mathematical aspects of quantum theory, computational genomics, image processing and medical imaging, inverse problems, combinatorial optimization, control, robotics, shape optimization, turbulence, hurricanes, microchip failure, MEMS, biodemography, population genetics, phylogenetics, and computational approaches to historical linguistics. Within the department we also have a Laboratory for Mathematical and Computational Biology.


Course Descriptions
Courses offered by the Statistics Department
Courses offered by the Computer Science Department
Courses offered by the Industrial Engineering and Operations Research Department

Undergraduate upper division courses

Math C103. Introduction to Mathematical Economics.
Math 104, H104. Introduction to analysis.
Math 105. Second course in analysis.
Math 110, H110. Linear algebra.
Math 113, H113. Introduction to abstract algebra.
Math 118. Fourier analysis, wavelets, and signal processing.
Math121A,B. Mathematical Tools for the Physical Sciences.
Math 123. Ordinary Differential Equations.
Math 126. Introduction to Partial Differential Equations.
Math 127. Mathematical and Computational Methods in Molecular Biology.
Math 128A,B. Numerical Analysis.
Math 170. Mathematical Methods for Optimization.
Math 172. Combinatorics.
Math 185, H185. Introduction to Complex Analysis.
Math 189. Mathematical Methods in Classical and Quantum Mechanics.

Graduate courses

Math 202A,B. Introduction to Topology and Analysis.
Math 203. Asymptotic Analysis in Applied Mathematics.
Math 204. Ordinary Differential Equations.
Math 205. Theory of Functions of a Complex Variable.
Math C218A,B. Probability Theory.
Math 220. Methods of Applied Mathematics.
Math 221. Advanced Matrix Computations.
Math 222A,B. Partial Differential Equations.
Math C223A,B. Stochastic Processes.
Math 224A,B. Mathematical Methods for the Physical Sciences.
Math 228A,B. Numerical Solution of Differential Equations.
Math 239. Discrete Mathematics for the Life Sciences.
Math 258. Classical Harmonic Analysis.
Math 273. Topics in Numerical Analysis.
Math 275. Topics in Applied Mathematics.


Applied Mathematics Seminar (Chorin, Strain, Wilkening)
Matrix Computations and Scientific Computing Seminar (Demmel, Gu, Parlett)
Computational Biology Seminar (Pachter, Sturmfels)
Probability Seminar (Chatterjee, Bhadimi)
Interdisciplinary Stochastic Processes Colloquium (Aldous)