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) | |
| Muhao Chen is an Assistant Professor at the Department of Computer Science, UC Davis, where he leads the Language Understanding and Knowledge Acquisition (LUKA) Group (https://luka-group.github.io/).He received his Ph.D. from the Department of Computer Science at UCLA in 2019, and B.S. in Computer Science from Fudan University in 2014. His research focuses on robust and accountable ML. Most recently, his group has been focusing on accountability and security problems of large language models and multi-modal language models. His work has been recognized with an NSF CRII Award, faculty research awards from Amazon (twice) and Cisco, Outstanding Paper Awards at EMNLP (twice), an ACM SIGBio Best Student Paper Award, and funding support from multiple NSF, IARPA and DARPA programs. Additional information is available at https://muhaochen.github.io/ [Related Courses] | |
| 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] |