Department of Mathematics, UCLA
dabh -at- alumni.stanford.edu
Brief bio: David Hyde is now a visiting faculty member at UCLA. He was first a Regents Scholar at UCSB, earning a B.S. in Mathematics with highest honors at age 19. Hyde then earned a Ph.D. in Computer Science (with Distinction in Teaching) from Stanford, where he was a DoD NDSEG Fellow and a Gerald J. Lieberman Fellow. He also earned M.S. degrees in computer science and applied math. His research has been supported by the Army Research Lab, the Department of Energy, and BHP Billiton. In an earlier life he helped build successful technology companies in quantum computing, databases, and data science.
Full CV available upon request.
Research keywords: Computational Physics, Computer Graphics, Machine Learning, Data Science, HPC, Visualization, etc.
Research summary: I am interested in advancing simulation science, primarily in computational physics and computer graphics. Typically this takes the form of new numerical methods and novel algorithms for tackling simulation problems. However, recent work has included blending deep learning and computer vision techniques with more classical computational and applied mathematics—including leveraging numerical understanding to design new algorithms for learning and data science. I also maintain an interest in high-performance computing, particularly as it relates to designing scalable numerical algorithms for simulation and learning. Furthermore, I have occasionally dabbled in low-level systems work which can make simulation codes more efficient, as well as high-level work on understanding how users interact with novel simulations and simulation systems. Although my focus is on physical simulation and the synergies between simulation, learning, and data, I believe it is fruitful to maintain a holistic view of simulation research and to address the biggest challenges facing simulation science on whatever "layer of the stack" they occur.