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 |
|---|---|
| Computational and Mathematical Biology. Biophysical models of DNA. Topological Data Analysis (TDA) in genetics. Modeling of DNA under spatial confinement: design of viral particles for nanotechnological purposes and modeling of mitochondrial DNA in trypanosomes. Application of TDA to cancer genetics. [Related Courses] | |
| Numerical linear algebra (theory, algorithm development & analysis) | |
| Multiscale asymptotics for PDEs; atmospheric science; fluid dynamics. [Related Courses] | |
| I study representation theory, algebraic geometry, and combinatorics, as well as applications of algebraic topology to data science. I am especially interested in formulating a density-based version of persistent homology using simulated data sets from statistical mechanics and other random processes. | |
| Computational neuroscience; mathematical biology; machine learning; algorithms; distributed computing; graph theory; dynamical systems; high-dimensional probability and statistics | |
| Discrete mathematics, algorithms in algebra and geometry. [Related Courses] | |
| Imaging, inverse problems, wave propagation, turbulent transport. | |
| Mathematical Physics; Quantum Information [Related Courses] | |
| My research area is numerical analysis and scientific computing, particularly the numerical solution of partial differential equations and numerical linear algebra. I focus primarily on applications in wave scattering and material science. I am interested in developing new algorithms for the fast and accurate solution of partial differential equations. [Related Courses] | |
| Probability; cellular automata. [Related Courses] | |
| Numerical analysis and methods for molecular modeling, self-assembly in molecular ensembles, computational molecular and statistical mechanics, radiation damage in crystalline materials, vortex dynamics, perturbation techniques for nonlinear oscillators and soliton systems, Josephson systems, superconducting device physics, phase-locking. | |
| Mathematical biology, mathematical modeling, Newtonian and non-Newtonian fluid dynamics, and numerical analysis. | |
| Mathematics, topology, differential geometry, mathematical physics. | |
| Nonlinear wave propagation, continuum mechanics, singular perturbation methods, and nonlinear hyperbolic partial differential equations. | |
| My research interests are in Partial Differential Equations, specifically those
equations which arise from fluid dynamics. Within this vast field, I have thus
far concentrated on obtaining rigorous results related to the boundary layer
theory, which describes the behavior of a viscous fluid in the vicinity of a solid
wall at high Reynolds number. | |
| Mathematical optimization, in particular integer
programming and mixed-integer programming; computational discrete
mathematics [Related Courses] | |
| Mathematical physiology, neuroscience, cardiac electrophysiology. | |
| Markov chain Monte Carlo, random walks on graphs, randomized algorithms, probability on trees. | |
| Mathematics. Algebra, geometry, global analysis and mathematical physics. | |
| Mathematical physics; statistical mechanics; quantum spin systems; rigorous results in quantum mechanics and condensed matter physics; quantum information and computation. [Related Courses] | |
| Numerical solutions of PDEs, thermodynamics, droplets, geophysics, mantle convection. | |
| Theoretical computer science. Foundations of data science. Matrix computations. Machine learning. Convex geometry. Optimization. [Related Courses] | |
| Applied and computational harmonic analysis, statistical signal processing, image analysis, feature extraction, pattern recognition, potential theory,elliptic eigenvalue problems, geophysical inverse problems, human and machine perception. [Related Courses] | |
| Combinatorics, representation theory, mathematical physics, Markov chains [Related Courses] | |
| Mathematics of data science, computational imaging, computer vision, machine learning, fast and robust algorithms for cryo-electron microscopy (Cryo-EM) imaging and other 3D reconstruction problems [Related Courses] | |
| Hyperbolic PDE, moving free-boundary and interface problems,
mathematical fluid dynamics [Related Courses] | |
| Random matrix theory; probability theory; mathematical physics; combinatorics. [Related Courses] | |
| Numerical analysis, applied harmonic analysis, digital signal processing, approximation theory, scientific computing. [Related Courses] | |
| Shock waves, general relativity, applied analysis. [Related Courses] | |
| Scientific computing, solid mechanics, fluid
mechanics, computer graphics | |
| I am a Professor in the departments of Mathematics and of Microbiology and Molecular Genetics at UC Davis. I am a mathematical biologist who specializes in the applications of topological methods and computational tools to the study of DNA packing, DNA-protein interactions, and DNA rearrangements. | |
| Theoretical foundations of data science. Statistical models ranging from community detection in networks to determination of 3-dimensional molecular structure in cryo-electron microscopy. [Related Courses] | |
| Geometric measure theory and its application; optimal mass transport problems; mathematical biology; geometric variational problems in singular spaces. |