Bernd Bohnet
Research Scientist at Google DeepMind
Nijmegen, Gelderland, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolBernd Bohnet is a research scientist based in Nijmegen with seven years of professional experience and a long academic pedigree culminating in a Dr. rer. nat. in computer science from Universität Stuttgart. He has held research and academic roles across Europe and the US before joining Google and now Google DeepMind, blending deep theoretical expertise with practical ML engineering. Bernd contributes to prominent open-source ML tooling—most notably improving and modernizing examples in Google's Flax library by migrating code to the linen API, fixing tests, and updating documentation. His background spans postdoctoral and faculty positions, indicating strong publication and mentoring experience alongside hands-on model and code development. Colleagues know him for translating research-grade ideas into robust, maintainable code and for smoothing adoption hurdles in widely used libraries.
7 years of coding experience
9 years of employment as a software developer
Dr. rer. nat., Informatik, Dr. rer. nat., Informatik at Universität Stuttgart
Github Skills (8)
machine-learning10
nlp10
jax10
flax10
python9
opt7
optim7
tensorflow4
Programming languages (2)
Github contributions (5)
google/flax
Mar 2020 - Feb 2022
Flax is a neural network library for JAX that is designed for flexibility.
Role in this project:
ML Engineer
ML EngineerContributions:13 reviews, 17 commits, 16 PRs in 1 year 11 months
Contributions summary:Bernd's contributions center on improving and refactoring the examples for the `flax` library, specifically focusing on the `nlp_seq` example. Their work involved updating documentation, correcting imports, fixing tests, and migrating the code to use the newer `linen` API. They also replaced the deprecated optimizer and made various code changes to support the `linen` migration.
deep-learningneural-networksneural-networkflaxjax
bohnetbd/flax
Mar 2020 - Mar 2020
Flax is a neural network library for JAX that is designed for flexibility.
Contributions:7 pushes, 1 branch in 3 days
deep-learningneural-networksneural-networkflaxjax
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