Felix Zimmermann is a PhD researcher at PTB in Berlin combining training in medicine and physics with 11 years of experience applying machine learning to imaging. He develops robust, model-aware reconstruction algorithms for quantitative MRI and publishes and presents his work at conferences. His background includes hands-on X-ray free-electron laser experiments and data analysis at Stanford, SLAC and SACLA, where he planned beamtime and prepared nanoscale reference samples. Felix contributes to major open-source ML infrastructure like PyTorch, improving type hints and optimizer tests, reflecting practical ML engineering skills. Comfortable across lab, clinical and computational settings, he brings a rare mix of clinical insight, experimental optics expertise and production-oriented ML development.
11 years of coding experience
2 years of employment as a software developer
Staatsexamen Medizin, Staatsexamen Medizin at Charité - Universitätsmedizin Berlin
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
ML Engineer
Contributions:5 reviews, 3 PRs, 36 comments in 3 years 11 months
Contributions summary:Felix contributed to the PyTorch repository by addressing issues related to type hints for tensor arithmetic and the LBFGS optimizer. They modified type hints in the `gen_pyi.py` tool to reflect the correct signatures for tensor operations and also added a test case for LBFGS with complex parameters, demonstrating knowledge of the optimizer's behavior. Additionally, the user made improvements to the code by adjusting assert statements and ensuring proper handling of running mean and variance values.
Simulating, Reconstructing and Analysing Data for FEL IDI Experiments
Contributions:412 commits, 11 PRs, 428 pushes in 2 years 6 months
felidiincoherent-imagesreconstructionsimulation
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