Johanna Sommer
Machine Learning Research Engineer at Pruna AI
Munich, Bavaria, Germany
Summary
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Senior🎓
Top SchoolJohanna Sommer is a Machine Learning Research Engineer and PhD candidate at TU Munich with nine years of experience bridging industrial research and academic ML. She progressed from a dual study program and multiple IBM research internships—working on AutoML, meta-learning for gradient boosting, and large-scale sparse matrix techniques—to research roles at TUM and now Pruna AI. Her work blends hands-on systems thinking with theoretical rigor, often focusing on efficient, scalable ML algorithms and practical deployment considerations. Notably, she contributed to Apache SystemML during an IBM Almaden internship, signaling early commitment to impactful open-source and large-scale ML. Based in Munich, she aims to translate doctoral research into production-ready solutions that improve model selection and training efficiency.
9 years of coding experience
3 years of employment as a software developer
Bachelor of Applied Science (BASc) Applied Computer Science, Bachelor of Applied Science (BASc) Applied Computer Science at Baden-Wuerttemberg Cooperative State University (DHBW)Doctor of Philosophy - PhD Machine Learning, Doctor of Philosophy - PhD Machine Learning at Technical University of Munich
German, English, Italian
Github Skills (34)
lifecycle10
combinatorial-optimization10
dml10
java10
operations-research10
python9
machine-learning9
optimization9
science9
big-data8
data-science8
xor7
rubiks-cube7
generalization7
sparse6
Programming languages (6)
Github contributions (5)
johannaSommer/generalization-neural-co-solvers
Jan 2022 - Nov 2022
Official Repository for the ICLR 2022 paper "Generalization of Neural Combinatorial Solvers through the Lens of Adversarial Robustness"
Contributions:10 commits, 3 PRs, 8 pushes in 9 months
pytorchiclrrobustnessgeneralizationdeep-learning
PrunaAI/pruna
Mar 2025 - Apr 2025
Pruna is a model optimization framework built for developers, enabling you to deliver faster, more efficient models with minimal overhead.
Contributions:12 reviews, 24 PRs, 29 pushes in 16 days
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