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Education
Ph.D., Computer Science, Stanford University
M.S., Computational and Mathematical Engineering, Stanford University
M.S., Computer Science, Stanford University
B.S., Mathematics, University of California, Santa Barbara
Research Focus
Professor Hyde leads the Simulation, Optimization, and Learning (SOL) laboratory at Vanderbilt University. His group creates new algorithms and numerical methods for problems in and at the intersections of these three fields. A particular emphasis is on developing techniques for simulating physical phenomena that involve solids and fluids. These simulations are useful for engineering and computer graphics, as well as for providing data and constraints for learning-based systems. Hyde and his group also build cloud computing platforms, create scalable heterogeneous implementations of algorithms, and develop practical software, libraries, and datasets relevant to their research focus. The SOL lab ultimately seeks to illuminate the synergies between simulation, learning, and data, and to leverage these connections to solve complex real-world challenges.
Publication Highlights
“A Deep Conjugate Direction Method for Iteratively Solving Linear Systems.” A. Kaneda, O. Akar, J. Chen, V.A.T. Kala, D. Hyde, J. Teran. International Conference on Machine Learning (ICML), 15720-15736 (2023).
“A Unified Approach to Monolithic Solid-Fluid Coupling of Sub-Grid and more Resolved Solids.” D.A.B. Hyde, R. Fedkiw. Journal of Computational Physics 390, 490-526 (2019).
“Automated Synthesis of Quantum Algorithms via Classical Numerical Techniques.” Y. Huang, B. Grossman-Ponemon, D. Hyde. ACM Transactions on Quantum Computing (TQC) 6 (4), 1-24 (2025).