Summary
Justin Dong is a postdoctoral research scholar at Lawrence Livermore National Laboratory with 10 years of experience in scientific computing, numerical analysis, and machine learning, focused on neural-network approaches for approximating PDEs. He earned a PhD in Applied Mathematics from Brown University (NSF GRFP recipient) and combines rigorous analysis with hands-on implementation of high-order time integration methods in the E3SM climate model. His work spans GPU-accelerated discontinuous Galerkin solvers, performance-portable C++ frameworks, and physics-informed neural closures for complex suspension and groundwater problems. Based in Livermore, CA, he bridges theoretical development and production-scale climate modeling, bringing a rare mix of deep numerical insight and practical, parallel-computing experience.
10 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD Applied Mathematics, Doctor of Philosophy - PhD Applied Mathematics at Brown University
Bachelor of Science (B.S.) Mechanical Engineering, Bachelor of Science (B.S.) Mechanical Engineering at Rice University