Developing advanced fast machine learning, stochastic, and deterministic numerical methods for data science and scientific computing applications
Focus on understanding complex physical phenomena, especially quantum phenomena, electromagnetic processes, wave scattering in random media, and stochastic controls
Research includes deep neural network algorithms for scientific computing, wideband and multiscale spectral-bias-free learning for high-dimensional quantum systems, high-frequency wave scattering in random media, fluid dynamics, and stochastic controls
Other interests: fast multipole methods for wave interactions in layered media, hierarchical random compression for kernel matrices, stochastic computing, computational probability, Feynman-Kac representations of PDEs, E&M polarizability tensors for complex-shaped particles
Also works on computational biology, solvation, fast electrostatics algorithms, computational electromagnetics for metamaterials, quantum transport (Wigner transport and NEGF methods), adaptive wavelet/multiscale methods
Employs Monte Carlo/stochastic methods, spectral methods, integral equation methods, and discontinuous Galerkin methods