Samuel Burbulla
Software Engineer at Google
Munich, Bavaria, Germany
Summary
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Top SchoolSamuel Burbulla is a software engineer and applied mathematician with a PhD in mathematical modelling and eight years of experience bridging scientific research and production ML. He specializes in physics-informed deep learning and operator learning for simulating physical systems, combining rigorous numerical simulation expertise from his doctoral work with hands-on ML engineering. Samuel has contributed to open-source scientific ML (notably enhancements to the deepxde library supporting PI-DeepONet across PyTorch and TensorFlow) and has moved research into practice at appliedAI and now Google’s ML Red Team. Based in Munich, he brings a pragmatic blend of research depth, software craftsmanship, and experience in cross-institutional collaborations from academia to industry. An underappreciated strength is his ability to translate complex PDE-based modelling concepts into robust, debuggable code and reproducible examples.
7 years of coding experience
7 years of employment as a software developer
Master of Science - MS, Mathematics, Master of Science - MS, Mathematics at Albert-Ludwigs-Universität Freiburg im BreisgauDr. rer. nat., Mathematik, Dr. rer. nat., Mathematik at Universität Stuttgart
Github Skills (8)
pytorch10
deep-learning10
tensorflow10
scientific-machine-learning9
pde9
neural-network9
python8
jax4
Programming languages (4)
Github contributions (5)
lululxvi/deepxde
May 2023 - Aug 2023
A library for scientific machine learning and physics-informed learning
Role in this project:
ML Engineer
ML EngineerContributions:6 reviews, 7 PRs, 15 comments in 3 months
Contributions summary:Samuel primarily contributed to the deepxde library by implementing features and fixing bugs related to the pytorch and tensorflow backends. Their work included adding auxiliary variables, supporting PI-DeepONet, and addressing issues in the function spaces. The user also added and fixed examples demonstrating the use of PI-DeepONet for solving differential equations.
deeponetoperatorpaddlescientific-machine-learningtensorflow
aai-institute/continuiti
Dec 2023 - Aug 2024
Learning function operators with neural networks.
Contributions:3 releases, 68 reviews, 153 PRs in 8 months
neural-operatorsphysics-informed-mltransferlab
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