Wednesday 10 December 2025
Machine learning applied to the physical sciences (especially identification and control of quantum systems), dynamics, mechanics, numerical methods, and scientific comuting
Harmonic analysis, geometric measure theory, and educational data analysis
Mathematical modeling of biological systems, uncertainty quantification, machine learning, deep-learning, data-science
Linear and nonlinear waves, solar-energy conversion, PDEs asymptopic analysis & perturbation methods
Fluid-structure interactions, multiphase flows, numerical methods for PDEs, applications in ecology and oceanography
Waves in random media, inverse problems, asymptotic analysis and perturbation methods
Stochastic modeling, scientific computing, computational fluid dynamics, machine-learning-based surrogate modeling, chemical applications
Low-dimensional topology and geometry, mathematics education
Nonlinear optimization, numerical linear algebra, compressed sensing, and image processing
Partial differential equations (degenerate elliptic equations, free boundary problems, mathematics education (student learning in introductory and bridge courses, anxiety in the mathematics classroom)
Large-scale inverse problems, PDE-constrained optimization, uncertainty quantification, optimal experimental design
Mathematical and computational biology, machine learning, functional genomics
Mathematical modeling of biological systems, machine learning, inverse problems
Imaging with waves in complex media with applications in remote sensing, geophysics, microwave imaging and optics
Numerical analysis, structure-preserving discretizations, Bayesian inference, Hamiltonian Mote Carlo, scientific computing and machine learning
