Stephan Hoyer
Member Of The Technical Staff at Xarray
San Francisco, California, United States
Summary
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Rockstar🎓
Top SchoolStephan Hoyer is a research scientist and software engineer with 13 years of experience building numerical and ML-driven tools for weather, climate, and physics. He led research and engineering teams at Google on AI-for-science and now applies differentiable physics and AI at Periodic Labs, while also founding and guiding the widely used Xarray project with a 21-person contributor community and >$500k in grant funding. His open-source contributions have shaped core scientific Python projects—NumPy, JAX, Dask, Pandas, netCDF4 and Bottleneck—improving performance, array semantics, and file-backend interoperability. Trained as a theoretical physicist (PhD, UC Berkeley), he combines deep domain knowledge in numerical methods and spatiotemporal statistics with pragmatic engineering, for example implementing new NumPy functions and stabilizing complex I/O interactions in xarray. Colocated in San Francisco, he is as comfortable prototyping differentiable CFD in JAX as he is optimizing large-scale data pipelines for geoscience.
13 years of coding experience
17 years of employment as a software developer
PhD, Theoretical Physics, PhD, Theoretical Physics at University of California, BerkeleyBA, Physics, BA, Physics at Swarthmore College
Stackoverflow
4,872reputation
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37answers
7questions
forms
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proxy
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Github Skills (70)
cfml10
python10
testing10
proxy10
c1110
c1710
cfc10
parallel-computing10
cython10
file-processing10
numerical-optimization10
cfengine10
netcdf10
optimization10
code-optimization10
Programming languages (19)
Github contributions (5)
pydata/xarray
Jan 2014 - Oct 2022
N-D labeled arrays and datasets in Python
Role in this project:
Back-end Developer
Back-end DeveloperContributions:34 releases, 359 reviews, 1589 commits in 8 years 10 months
Contributions summary:Stephan's contributions primarily focused on maintaining and improving the xarray library, a Python library for labeled multi-dimensional arrays and datasets. Their commits demonstrate a strong understanding of the library's internals and its integration with the broader scientific Python stack by fixing bugs, improving code quality, and addressing issues related to performance and compatibility with other libraries such as pandas, netCDF4 and dask. The contributions range from debugging complex interactions with file backends to enhancing the stability and reliability of xarray operations.
pythonnetcdfnumpydaskarrays
google/jax-cfd
Mar 2021 - Jul 2022
Computational Fluid Dynamics in JAX
Role in this project:Back-end Developer
Back-end DeveloperContributions:3 releases, 1 review, 25 commits in 1 year 4 months
Contributions summary:Stephan primarily contributed to the `jax-cfd` repository, focusing on computational fluid dynamics (CFD) simulations using JAX. Their contributions included internal code changes, likely involving refactoring and optimization within the codebase. The commits demonstrate involvement in testing and modifying advection methods, implementing improvements to core numerical methods, and updating build configurations and installation processes. These changes aimed to improve the performance, and possibly the accuracy of CFD simulations.
cfdfluidtpufluid-dynamicscomputational-fluid-dynamics
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