Maria Teleńczuk
Senior Research Scientist In ML at Owkin
Paris, Ile-de-France
Summary
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Rockstar🎓
Top SchoolMaria Teleńczuk is a Senior Research Scientist in ML based in Paris with 13 years of experience bridging computational neuroscience, federated learning, and open-source software engineering. At Owkin she leads agentic AI engineering and collaborates with pharma on federated and spatial transcriptomics projects while maintaining and developing community tools like PyDESeq2, FLamby and Substra. Her background includes a PhD in computational neuroscience and research roles at Inria and CNRS where she contributed to scikit-learn and built reproducible neurocomputational pipelines. An active community organizer as co-lead of PyLadies Paris, she combines rigorous academic reproducibility (published work with Docker images) with practical ML production experience and notable contributions improving scikit-learn examples and documentation.
12 years of coding experience
10 years of employment as a software developer
Master of Science (M.Sc.), Computational Neuroscience, Master of Science (M.Sc.), Computational Neuroscience at Humboldt-Universität zu BerlinDoctor of Philosophy (PhD), Computational Neuroscience, Doctor of Philosophy (PhD), Computational Neuroscience at Sorbonne UniversityBachelor of Science (Honours), Software Development, Bachelor of Science (Honours), Software Development at Munster Technological University
Polish, English, French
Github Skills (7)
data-analysis10
scikit-learn10
machine-learning10
python10
data-science10
documentation10
scikit10
Programming languages (5)
Github contributions (5)
scikit-learn/scikit-learn
Feb 2019 - Sep 2022
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:21 reviews, 35 commits, 42 PRs in 3 years 7 months
Contributions summary:Maria's contributions primarily involve modifying and improving documentation and examples within the scikit-learn repository. They updated examples to use the diabetes dataset instead of the Boston dataset, and improved descriptions of datasets like the linnerud dataset. Furthermore, the user corrected label orders and made changes to the partial dependence plots.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
ramp-kits/stroke_lesions
Feb 2019 - May 2021
Contributions:196 commits, 6 PRs, 75 pushes in 2 years 3 months
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