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.
| Name | Research/Related Courses |
|---|---|
| Numerical linear algebra (theory, algorithm development & analysis) | |
| Network theory, statistical physics, computational science, probability, applied math, cellular automata, and networking protocols. | |
| Molecular computing, self-assembly, chemical reaction networks, distributed computing, theory of computing, algorithmic information theory, probability [Related Courses] | |
| Numerical methods of quantum mechanics; Large-scale parallel computing; Molecular dynamics. [Related Courses] | |
| Quantum Information theory, Quantum Computation, Matrix Analysis [Related Courses] | |
| My 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:
http://www.cs.ucdavis.edu/people/faculty/koehl.html
in CS and
http://genomecenter.ucdavis.edu/koehl_cv.html
at the Genome Center. | |
| Areas of interest include theoretical computer science; applied probability; statistics. My main line of research focuses on fundamental statistical problems, and I ask both statistical and computational questions for these problems. [Related Courses] | |
| Network resource management, optimization, machine learning. [Related Courses] | |
| Theoretical computer science [Related Courses] | |
| Geophysical fluid dynamics; dynamical systems; ocean science; climate science. [Related Courses] |