Chak Shing Lee is a Computational Mathematician in the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory (LLNL). His research interests include multilevel/multiscale methods and their applications in subsurface flow simulations, element-based algebraic multigrid methods, numerical discretizations for partial differential equations, symbolic regression, and interpretable artificial intelligence. He has contributed to smoothG (C++ library for graph Laplacian coarsening), ParELAG (C++ library for element agglomeration algebraic multigrid), MFEM (C++ library for finite element methods), and DSO (python package for symbolic optimization tasks using deep learning and reinforcement learning).

Ph.D. Mathematics, Texas A&M University

M.Phil. Mathematics, Chinese University of Hong Kong

B.Sc. Mathematics, Chinese University of Hong Kong

A list of Chak Shing’s publications can be found in his Google Scholar page.