Joseph Kleinhenz
Senior Machine Learning Scientist at Genentech
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolJoseph Kleinhenz is a Senior Machine Learning Scientist with 11 years of experience applying high-performance computing, quantum computing concepts, and ML to scientific problems across national labs and industry. He holds a PhD in Physics and Scientific Computing from the University of Michigan and transitioned from developing parallel C++ solvers and Julia packages for non-equilibrium condensed-matter simulations to production ML roles at Genentech. At Berkeley Lab he deployed data access and ML services for X-ray spectroscopy, and his open-source contributions include significant, performance-focused improvements to HDF5.jl such as complex number and variable-length string support. Based in San Francisco, he blends research-grade numerical methods and supercomputer-scale experience with pragmatic engineering for deployable ML systems, often surfacing tooling improvements that quietly improve data ergonomics for scientific workflows.
11 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Physics and Scientific Computing, Doctor of Philosophy (Ph.D.) Physics and Scientific Computing at University of MichiganBachelor’s Degree Physics, Bachelor’s Degree Physics at Duke University
Stackoverflow
1reputation
0reached
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Github Skills (2)
julia10
hdf10
Programming languages (6)
Github contributions (5)
JuliaIO/HDF5.jl
Aug 2019 - Nov 2020
Save and load data in the HDF5 file format from Julia
Role in this project:
Back-end Developer
Back-end DeveloperContributions:9 reviews, 14 commits, 11 PRs in 1 year 2 months
Contributions summary:Joseph primarily contributed to enhancing the `hdf5.jl` library by adding features and fixing bugs. They implemented complex number support, improved hyperslab support, and optimized the reading of compound datatypes. Additionally, the user made significant changes to improve the ergonomics and performance of the library. These changes included reading compound datasets as named tuples, supporting variable-length strings, and unpadding fixed-length strings.
file-formatjulia-languagedata-storagehdf5hdf5-library
kleinhenz/julia
Jan 2017 - Sep 2017
Contributions:49 pushes, 6 branches in 8 months
julia-languageapproachtechnical-computingcomputingjulia
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