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 |
|---|---|
| Time series analysis, structural break analysis, theoretical/mathematical questions arising in
fields of application, such as economics, finance and environmental science.
[Related Courses] | |
| Inferential and computational issues in non-parametric statistics, high-dimensional statistics, network analysis and stochastic optimization. [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] | |
| 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] | |
| Methods and theory in high-dimensional statistics, robust statistical learning, network data analysis, and signal processing.
[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] | |
| Functional Data Analysis, Semiparametric Modelling, Applications in Biodemography, Genetics, Medicine, e-Commerce and Finance. [Related Courses] | |
| Dimension reduction methods; functional data analysis; longitudinal data analysis; nonparametric functional estimation; aging research; survival analysis. [Related Courses] |