Selected Talk Slides

• Some high dimensional sampling with applications in scientific computing [Slide]
• Randomized and Sparse Cholesky for learning with Gaussian processes [Slide]
• Fast, Multimodal, Derivative-Free Bayes Inference with Fisher-Rao Gradient Flows [Slide]
• Design of Gradient Flows for Sampling: Energy Functionals, Invariance, and Gaussian Approximation [Slide]
• On Multiscale and Statistical Numerical Methods for PDEs and Inverse Problems [Slide]
• Exponentially Convergent Multiscale Methods for solving elliptic and Helmholtz’s equation [Slide]
• Developments of Multiscale and Probabilistic Methods for Solving PDEs and Inverse Problems [Slide]
• Consistency of Hiearchical Parameter Learning: Empirical Bayesian and Kernel Flow Approaches [video1] [video2] [Slide]