Director Of Artificial Intelligence Services at X-energy
Washington, District of Columbia, United States
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Summary
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Derek Gaston is a computational science leader with 19 years of experience building and scaling high-performance simulation software for nuclear energy and beyond. He founded the MOOSE multiphysics framework at Idaho National Laboratory in 2008—a project that earned an R&D100 award and underpins multiple flagship tools for fuel performance, materials, neutronics and thermal fluids—and has contributed low-level fixes to libMesh and distributed-mesh features that improved robustness for large-scale runs. A Ph.D. from MIT with strong applied-math and CS roots, he blends hands-on backend engineering with strategic roles across DOE and industry, including senior advisory work on AI-enabled reactor deployment and export-control policy. Now leading AI services at X-energy, Derek is applying cloud and HPC-driven AI to accelerate next-generation reactor delivery for data centers and industrial users, while also founding a small venture that signals an entrepreneurial streak beyond national labs.
19 years of coding experience
20 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computational Science, Doctor of Philosophy (Ph.D.), Computational Science at Massachusetts Institute of Technology
Master’s Degree, Computational and Applied Mathematics, Master’s Degree, Computational and Applied Mathematics at The University of Texas at Austin
Bachelor of Science (BS), Computer Science, Bachelor of Science (BS), Computer Science at Missouri University of Science and Technology
Contributions:2 reviews, 274 commits, 38 PRs in 15 years 7 months
Contributions summary:Derek primarily focused on modifying and improving the libMesh Finite Element Library, particularly its ability to handle physical parameters and incorporate stopping events for the inverse map. This included making changes to safer parameter handling of speed and frequency values, incorporating additional logging, and adding code for the Variational Smoother class. Furthermore, the user implemented features to add and support additional solutions and also modify the way the Mesh Base is built to take into consideration different coordinate systems, thus improving the overall functionality and error handling capabilities of the library.
Contributions:123 reviews, 2143 commits, 325 PRs in 14 years 8 months
Contributions summary:Derek primarily focused on fixing issues related to memory management and data handling within the MOOSE framework. They addressed problems in data serialization, particularly for restart files, ensuring more robust solutions, and made the software more efficient, likely in service of scientific computing. Additionally, the user made changes to make it so that a custom partitioner can work with a distributed mesh.
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Derek Gaston - Director Of Artificial Intelligence Services at X-energy