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 |
|---|---|
| Molecular dynamics (MD) simulations as well as methods projects developing state-of-the-art enhanced sampling methods for MD simulations. [Related Courses] | |
| Analytical, computational and experimental studies of turbulent flame propagation; diamond synthesis via chemical vapor disposition; combustion instabilities; development of algorithms for the computational study of reactive-flow dynamics | |
| 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] | |
| Time series analysis, structural break analysis, theoretical/mathematical questions arising in
fields of application, such as economics, finance and environmental science.
[Related Courses] | |
| Computational biology, mathematical modeling of
biological systems, statistical signal processing and inverse problems with applications to genomics and structural biology, machine learning. In particular, I am interested in using probabilistic models and statistical inference to predict RNA structural dynamics from biochemical structure probing data and biophysical principles. | |
| Numerical linear algebra (theory, algorithm development & analysis) | |
| Inferential and computational issues in non-parametric statistics, high-dimensional statistics, network analysis and stochastic optimization. [Related Courses] | |
| 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 | |
| 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] | |
| Numerical modeling in Atmospheric Science. | |
| Physics of computation, dynamical systems, statistical mechanics, structural complexity, evolutionary dynamics, machine learning, and distributed robotics. [Related Courses] | |
| Network theory, statistical physics, computational science, probability, applied math, cellular automata, and networking protocols. | |
| Discrete mathematics, algorithms in algebra and geometry. [Related Courses] | |
| My current research focuses on the following areas: 1. Statistical and algorithmic applications of random matrix theory, spin glass theory and integrability theory; 2. Mathematical and statistical foundation for manifold learning and machine learning; 3. Non-stationary time series analysis and functional time series analysis. [Related Courses] | |
| Our lab is interested in neural mechanisms underlying higher-order brain functions linking perception and action, especially decision making. We are working on answering the question how the brain processes sensory information and combines it with other information in order to select what action to take next and when to take that action. So far, these mechanisms have mainly been studied from a psychological point of view, whereas neurophysiologists have largely concentrated on lower-level mechanisms. We are trying to bridge this gap by utilizing both behavioral and neurophysiological methods and by using mathematical models for exploring potential neural mechanisms. | |
| Spatial pattern formation; geo-evolutionary feedbacks; reaction diffusion models. [Related Courses] | |
| Molecular computing, self-assembly, chemical reaction networks, distributed computing, theory of computing, algorithmic information theory, probability [Related Courses] | |
| Network optimization, decision making under uncertainty, and civil infrastructure systems management, with an emphasis on applied mathematics. | |
| Imaging, inverse problems, wave propagation, turbulent transport. | |
| Geometrical algorithms and representations; computer-aided design and manufacturing; numerical analysis and scientific computing; particle simulation schemes (Coulomb systems); geometrical optics. | |
| Mathematical Physics; Quantum Information [Related Courses] | |
| Computational and theoretical neuroscience | |
| 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. | |
| Linear and nonlinear systems; multi-input, multi-output feedback control systems; decentralized control design; robust and reliable control design. | |
| Mathematical biology, mathematical modeling, Newtonian and non-Newtonian fluid dynamics, and numerical analysis. | |
| Numerical methods of quantum mechanics; Large-scale parallel computing; Molecular dynamics. [Related Courses] | |
| Mechanical Aerospace Engineering. Transonic aerodynamics, computational fluid dynamics; numerical solutions of partial differential equations. | |
| Flow and transport processes in ground water and in the vadose zone; stochastic analysis of such processes in heterogeneous porous systems; numerical modeling; assessment and remediation of ground water contamination; nonpoint source pollution of ground water; geostatistics.
Check out this
site to view videos of a colloquium, Advanced Modeling Concepts for Environmental Sciences.
| |
| Mathematics, topology, differential geometry, mathematical physics. | |
| Environmental Studies and Mathematics. Theoretical population biology including mathematical ecology and mathematical genetics; qualitative theory of differential equations, differential delay equations, bifurcation theory. | |
| 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. | |
| Experimental and computational cardiac dynamics; stability of calcium control system; origins of arrhythmias; optimal population variability. | |
| Current research interests: Connection between optimization, MCMC sampling, dynamical system, interacting particle system | Blending machine learning with traditional numerical algorithms for: PDE, sampling, optimal transport, control | AI4Science: data-driven approaches to computational problems arising in physics & chemistry | |
| Theoretical Machine Learning and Applied Probability: Sequential Learning, Graphical Models, Optimization Theory. [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. | |
| Mathematical optimization, in particular integer
programming and mixed-integer programming; computational discrete
mathematics [Related Courses] | |
| Machine learning, optimization, and information theory. [Related Courses] | |
| Study of hydrodynamics and dispersion in coastal ocean, including bays, estuaries and nearshore. Dispersion problems related to environmental issues, e.g., water quality, population ecology, algal blooms, estuarine habitat. Hydrodynamic problems include stratified flow, surface wind forcing, flow-topography interactions (e.g., jets, wakes, boundary layers), and waves/tides/internal waves. [Related Courses] | |
| 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] | |
| Mathematical physiology, neuroscience, cardiac electrophysiology. | |
| Methods and theory in high-dimensional statistics, robust statistical learning, network data analysis, and signal processing.
[Related Courses] | |
| Network resource management, optimization, machine learning. [Related Courses] | |
| My main research areas are in high-dimensional statistics and machine learning, with a particular focus on bootstrap methods and randomized numerical linear algebra. [Related Courses] | |
| Hari Manikantan works on mathematical modeling of continuum mechanics, and is broadly interested in problems involving fluid dynamics, multiphase flows, elasticity, soft matter, numerical methods, rheology, biophysics, hydrodynamic stability, nonlinear dynamics, and pattern formation. His current research interests include the nonlinear rheology of fluid interfaces in lipid layers, emulsions, and foams; the configurational dynamics and stability of elastic microfilaments such as biopolymers; and the flow of non-Newtonian fluids in porous media with applications in plant vasculature. [Related Courses] | |
| Computationally investigating the topology and geometry of configuration spaces of hard disks and hard spheres on flat toruses. Constructing certain pseudometrics on the space of regular cell complexes generated by homogeneous spatial processes [Related Courses] | |
| My research interests include the development of numerical
methods for the solution of complex problems in engineering and science,
mostly at the continuum scale; Complex rheology: multiscale and
continuum methods for incompressible viscoelastic flows; Multiphase
flows: compressible and incompressible flows of fluids and
elastic-plastic solids in time-dependent domains, interfacial processes;
Constitutive modeling: equations of state, thermodynamic modeling,
symmetry invariants; and Charged systems: plasma physics, boundary
charge and bilayer problems.
[Related Courses] | |
| Theoretical computer science [Related Courses] | |
| Markov chain Monte Carlo, random walks on graphs, randomized algorithms, probability on trees. | |
| Functional Data Analysis, Semiparametric Modelling, Applications in Biodemography, Genetics, Medicine, e-Commerce and Finance. [Related Courses] | |
| 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 and asymptotic methods applied to problems in atmospheric dynamics and geophysical fluid dynamics. | |
| Dr. Nuño is interested in the application of statistics and applied mathematics to solve public health challenges, reduce health disparities, and improve patient health outcomes. Her expertise lies at the interface of mathematical modeling, biostatistics, data science, epidemiology, and public health. She is interested in modeling of infectious diseases such as influenza and COVID-19, outbreak prediction and forecasting using wastewater surveillance. She is also very interested in understanding the health effects of wildfire exposure. [Related Courses] | |
| Non-Newtonian fluid mechanics; suspension mechanics. [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] | |
| Computational science & engineering; complex multiscale systems; driven threshold systems, including earthquakes and neural networks; statistical physics. | |
| 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] | |
| Environmental and natural resource economics, mathematical ecology and epidemiology, optimal control theory, spatial and dynamic analysis of economic-ecological models, bioeconomic analysis, panel data econometric estimation. | |
| Combinatorics, representation theory, mathematical physics, Markov chains [Related Courses] | |
| Microeconomic Theory, Game Theory, Experimental Economics, Industrial Organization, Bounded Rationality [Related Courses] | |
| Applications of dynamical systems and stochastic processes to ecology, epidemiology, and evolution. [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] | |
| Geophysical fluid dynamics; dynamical systems; ocean science; climate science. [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] | |
| Computational mechanics, fracture, finite element and meshfree methods,
maximum-entropy approximations, partition-of-unity finite element methods
in quantum mechanics, numerical analysis of PDEs,
physics-infomed neural networks [Related Courses] | |
| Shock waves, general relativity, applied analysis. [Related Courses] | |
| Scientific computing, solid mechanics, fluid
mechanics, computer graphics | |
| My research focuses largely on the design and evaluation of
innovative modeling techniques that improve the accuracy and eciency of
atmospheric general circulation models, and using these models to
answer scienti c questions related to atmospheric dynamics and the
interplay of dynamics on regional and global scales. [Related Courses] | |
| Valdovinos studies the structure and dynamics of ecological networks at ecological and evolutionary scales; including their resilience to biodiversity loss, biological invasions, climate change, and exploitation by humans. Her main research lines include plant-pollinator systems and food-webs, which she studies using a combination of mathematical and computational approaches. [Related Courses] | |
| 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. | |
| Dimension reduction methods; functional data analysis; longitudinal data analysis; nonparametric functional estimation; aging research; survival analysis. [Related Courses] | |
| 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] | |
| Heuristic search optimization techniques. Production and supply chain planning. Robust multivariate data analysis.
Stochastic Programming (integer). Bioinformatics. [Related Courses] | |
| Geometric measure theory and its application; optimal mass transport problems; mathematical biology; geometric variational problems in singular spaces. | |
| Vehicular traffic flow, network optimization and control of transportation systems. [Related Courses] |