David Hyde

PIC Assistant Adjunct Professor
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.

News/Media

Publications

On Obtaining Sparse Semantic Solutions for Inverse Problems, Control, and Neural Network Training

D. Hyde, M. Bao, R. Fedkiw
Submitted to Journal of Computational Physics, 2020

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An Implicit Updated Lagrangian Formulation for Liquids with Large Surface Energy

D. Hyde, S. Gagniere, A. Marquez-Razon, J. Teran
ACM TOG (SIGGRAPH Asia 2020 Technical Papers), 2020

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Improved Search Strategies with Application to Estimating Facial Blendshape Parameters

M. Bao, D. Hyde, X. Hua, R. Fedkiw

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A Hybrid Lagrangian/Eulerian Collocated Velocity Advection and Projection Method for Fluid Simulation

S. Gagniere, D. Hyde, A. Marquez-Razon, C. Jiang, Z. Ge, X. Han, Qi Guo, J. Teran
Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation 2020 (SCA 2020), 2020

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Assessing the Effects of Failure Alerts on Transitions of Control from Autonomous Driving Systems

E. Fu, D. Hyde, S. Sibi, M. Johns, M. Fischer, D. Sirkin
Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV 2020), 2020

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Is Too Much System Caution Counterproductive? Effects of Varying Sensitivity and Automation Levels in Vehicle Collision Avoidance Systems

E. Fu, M. Johns, D. Hyde, S. Sibi, M. Fischer, D. Sirkin
Proceedings of the 2020 ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2020), 2020

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Award

A Unified Approach to Monolithic Solid-Fluid Coupling of Sub-Grid and More Resolved Solids

D. Hyde and R. Fedkiw
Journal of Computational Physics, 2019-08

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VRGE: An Immersive Visualization Application for the Geosciences

D. Hyde, T. Hall, J. Caers
Proceedings of the 2018 IEEE Conference on Scientific Visualization (SciVis '18), 2018

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Assessing and Visualizing Uncertainty of 3D Geological Surfaces Using Level Sets with Stochastic Motion

L. Yang, D. Hyde, O. Grujic, C. Scheidt, J. Caers
Computers and Geosciences, 2019-01

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Distributing and Load Balancing Sparse Fluid Simulations

C. Shah, D. Hyde, H. Qu, P. Levis
Computer Graphics Forum (Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation 2018 (SCA 2018)), 2018

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Sharp interface approaches and deep learning techniques for multiphase flows

F. Gibou, D. Hyde, R. Fedkiw
Journal of Computational Physics, 2019

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FRC: A High-Performance Concurrent Parallel Deferred Reference Counter for C++

C. Tripp, D. Hyde, B. Grossman-Ponemon
Proceedings of the 2018 ACM SIGPLAN International Symposium on Memory Management (ISMM '18), 2018

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Code

A Robust Volume Conserving Method for Character-Water Interaction

M. Lee, D. Hyde, K. Li, R. Fedkiw
Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation 2019 (SCA 2019), 2019

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A Skinned Tetrahedral Mesh for Hair Animation and Hair-Water Interaction

M. Lee, D. Hyde, M. Bao, R. Fedkiw
IEEE TVCG, 2018

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Knotting Fingerprints Resolve Knot Complexity and Knotting Pathways in Ideal Knots

D. Hyde, J. Henrich, E. Rawdon, K. Millett
Journal of Physics: Condensed Matter (Special Issue on Knots), 2015

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