David Hyde PIC Assistant Adjunct Professor |
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. |
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. |
An Implicit Updated Lagrangian Formulation for Liquids with Large Surface Energy
ACM TOG (SIGGRAPH Asia 2020 Technical Papers), 2020
Computer Graphics Forum (SCA 2020), 2020
Assessing the Effects of Failure Alerts on Transitions of Control from Autonomous Driving Systems
Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV 2020), 2020
Proceedings of the 2020 ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2020), 2020
A Unified Approach to Monolithic Solid-Fluid Coupling of Sub-Grid and More Resolved Solids
Journal of Computational Physics, 2019-08
VRGE: An Immersive Visualization Application for the Geosciences
Proceedings of the 2018 IEEE Conference on Scientific Visualization (SciVis '18), 2018
Computers and Geosciences, 2019-01
Distributing and Load Balancing Sparse Fluid Simulations
Computer Graphics Forum (Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation 2018 (SCA 2018)), 2018
Sharp interface approaches and deep learning techniques for multiphase flows
Journal of Computational Physics, 2019
FRC: A High-Performance Concurrent Parallel Deferred Reference Counter for C++
Proceedings of the 2018 ACM SIGPLAN International Symposium on Memory Management (ISMM '18), 2018
A Robust Volume Conserving Method for Character-Water Interaction
Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation 2019 (SCA 2019), 2019
Knotting Fingerprints Resolve Knot Complexity and Knotting Pathways in Ideal Knots
Journal of Physics: Condensed Matter (Special Issue on Knots), 2015