About
I am a computational engineer focused on large-scale numerical optimization and simulation, with applications spanning physics, machine learning, and design automation. I have built high-performance computing systems and applied optimization frameworks for industrial-scale problems at Apple, Google X, and Lawrence Livermore National Laboratory.
My Ph.D. from UC Berkeley’s Multi-physics Simulation and Optimization Laboratory centered on stochastic and gradient-based optimization for non-convex numerical simulation. Earlier, I earned my B.Sc. in Chemical Engineering from Brigham Young University, concentrating on control theory and optimization.
Here are some snapshots of my public works:
Engineering Bits
Engineering Atoms
Graduate School Notes
I am currently compiling and transcribing all of my notes from graduate school into a numerical computing handbook. This is far from complete, but I do use this link to refer to equations and derivations.
You can find my dissertation at: