List of Faculty in the Graduate Group in Applied Mathematics (GGAM)

GGAM comprises faculty members from departments across the campus, including its home, the Department of Mathematics. Below is a brief description of faculty research, links to personal and departmental web pages plus some "Related Courses" which can serve as a general study guideline for students interested in research with a particular faculty member. Students who want a more complete description of a faculty member's research interests are encouraged to contact them.

Choose a department below or list all faculty
Biomedical Engineering Bodega Marine Laboratory
Center for Neuroscience Chemical Engineering
Chemical Engineering and Materials Science Civil and Environmental Engineering
Computer Science Department of Pharmacology
Economics Electrical and Computer Engineering
Environmental Science and Policy Evolution and Ecology
Graduate School of Management Land, Air and Water Resources
Materials Science & Engineering Mathematics
Mechanical and Aeronautical Engineering Mechanical and Aerospace Engineering
Microbiology and Molecular Genetics Molecular and Cellular Biology
Neurobiology, Physiology and Behavior Physics

NameResearch/Related Courses
Bai, ZhaojunNumerical linear algebra (theory, algorithm development & analysis)
D'Souza, RaissaNetwork theory, statistical physics, computational science, probability, applied math, cellular automata, and networking protocols.
Doty, DaveMolecular computing, self-assembly, chemical reaction networks, distributed computing, theory of computing, algorithmic information theory, probability
[Related Courses]
Gygi, FrançoisNumerical methods of quantum mechanics; Large-scale parallel computing; Molecular dynamics.
[Related Courses]
Koehl, PatriceMy research program focus on understanding protein structures. I am interested in characterizing their shapes using mathematical and computational approaches, and to use this information to improve our understanding of their stability. I am also interested in characterizing the subset of sequence space compatible with a protein structure: this is an indirect approach to understanding protein sequence evolution. In parallel, I am involved in the development of new algorithms for predicting the structure of a protein, based on its sequence. My department web pages are: in CS and at the Genome Center.
Liu, XinNetwork resource management, optimization, machine learning.
[Related Courses]