Jonas Geiping
Research Group Leader at ELLIS Institute Tübingen
Germany
Summary
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Rockstar🎓
Top SchoolJonas Geiping is a research group leader at the ELLIS Institute Tübingen and the Max Planck Institute for Intelligent Systems, bringing nine years of research experience at the intersection of mathematical optimization and deep learning. With a Doctorate in Computer Science and a Master’s in Applied Mathematics, he focuses on optimization-driven advances in ML and the security and privacy implications of those methods. His work spans theory and practice, including engineering contributions to efficient training of BERT-style models—optimizing compute, data pipelines, and deployment workflows. Jonas combines rigorous mathematical grounding with hands-on ML engineering, routinely moving ideas from analysis to reproducible code. Based in Germany, he is particularly interested in making ML both safer and more computationally efficient, a theme reflected in his open-source contributions.
9 years of coding experience
7 years of employment as a software developer
Master's degree, Applied Mathematics, Master's degree, Applied Mathematics at University of MünsterDoctor of Science, Computer Science, Doctor of Science, Computer Science at Universität Siegen
Github Skills (12)
data-preprocessing10
pytorch10
machine-learning10
nlp10
language-model10
python10
natural-language-processing10
transformers9
huggingface8
huggingface-hub8
cicd4
tensorflow3
Programming languages (5)
Github contributions (5)
JonasGeiping/cramming
Dec 2022 - Mar 2023
Cramming the training of a (BERT-type) language model into limited compute.
Role in this project:
ML Engineer
ML EngineerContributions:2 releases, 31 commits, 13 PRs in 2 months
Contributions summary:Jonas contributed to the development and improvement of a BERT-type language model. Their work included refactoring code to reduce thread usage for performance optimization. They also implemented new learning rate cooldown schedulers and modified the data preprocessing pipeline to optimize dataset handling. Additionally, the user appears to be involved in model deployment, including the option to push to Hugging Face Hub.
bertlanguage-modelenglish-languagemachine-learning
HendrikMuenster/superResolution
Oct 2016 - Aug 2017
Contributions:2 releases, 76 commits, 46 pushes in 10 months
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