Luke Kelley
Senior Software Engineer at Waymo
Berkeley, California, United States
Summary
👤
Senior🎓
Top SchoolLuke Kelley is a Senior Software Engineer and computational astrophysicist based in Berkeley with 12 years of experience building high-performance simulation and data-analysis systems for scientific and production settings. He has led interdisciplinary teams through landmark discoveries—most notably authoring the flagship analysis of the first low-frequency gravitational wave detection—and has secured over $2M in competitive research funding. Technically fluent across HPC, MPI/OpenMPI, parallelized C/C++ and Python/Cython codebases, he designs and optimizes large-scale numerical simulations and end-to-end analysis pipelines that achieved 100x speedups and 10x data reductions. At Waymo he now applies those skills to vehicle simulations, while his open-source contributions include usability improvements to SciPy’s statistical tools. He combines rigorous statistical inference and modern ML techniques (normalizing flows, simulation-based inference, random forests) with production software practices—unit testing, CI, and reproducible pipelines—to turn complex, high-dimensional science into actionable results.
12 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Astrophysics, Doctor of Philosophy - PhD, Astrophysics at Harvard UniversityUniversity of California Santa Cruz
Github Skills (6)
algorithms10
scipy10
python10
scientific-computing10
numpy10
documentation9
Programming languages (8)
Github contributions (5)
scipy/scipy
Nov 2015 - Nov 2015
SciPy library main repository
Role in this project:
Data Scientist
Data ScientistContributions:9 commits, 1 PR, 18 comments in 10 days
Contributions summary:Luke primarily contributed to the `scipy/scipy` repository by improving the documentation and functionality of the `binned_statistic` functions. Their commits involved fixing documentation errors, enhancing the return of bin indices, and expanding the functionality to handle multiple values arrays. These changes suggest a focus on improving the usability and flexibility of the library's statistical analysis tools.
scipypythonscientific-computing
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