David Kamensky
Research Scientist at Meta
Kirkland, Washington, United States
Summary
👤
Senior🎓
Top SchoolDavid Kamensky is a research scientist and computational mechanic with eight years of professional experience translating advanced numerical methods for partial differential equations into robust simulation software. He has bridged academia and industry—leading research groups and solver teams at UC San Diego and Coreform before joining Meta—focusing on finite element, immersed, and isogeometric techniques for fluids, solids, and fluid–structure interaction. David’s work spans from code generation that marries Python problem descriptions with efficient C++ kernels to practical solver engineering in production-grade simulation tools. He has a strong publication record in peer-reviewed venues and a PhD in Computational Science from UT Austin, reflecting deep theoretical and implementation expertise. Often finding value in cross-pollinating ideas, he has taken academic methods (e.g., immersed/isogeometric analysis) into commercial products, demonstrating an uncommon knack for turning research prototypes into scalable software.
8 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy - PhD, Computational Science, Engineering, and Mathematics (CSEM), Doctor of Philosophy - PhD, Computational Science, Engineering, and Mathematics (CSEM) at The University of Texas at AustinBachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at University of Virginia
Github Skills (22)
fenics10
computational-science10
hpc10
parallel-computing10
nurbs10
c-library10
cpp10
amr10
scalable10
scientific-computing10
multiphysics9
numba9
finite-element-analysis9
coupling8
python8
Programming languages (4)
Github contributions (5)
david-kamensky/ShNAPr
Aug 2019 - Sep 2022
Isogeometric analysis of shells using tIGAr.
Contributions:13 commits, 5 PRs, 12 pushes in 3 years 1 month
isogeometric-analysisisogeometricpythonshells
david-kamensky/discretely-div-free-subscales
Nov 2019 - Dec 2021
FEniCS and tIGAr-based scripts to study variational multiscale modeling with discretely divergence-free subscales.
Contributions:7 PRs, 19 pushes in 2 years 1 month
information-theoryssreflectmultiscale-modelingfenicsmultiscale
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