Jean Kossaifi
Senior Research Scientist at NVIDIA
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJean Kossaifi is a Senior Research Scientist at NVIDIA with 14 years of experience bridging academic rigor and industrial-scale AI research. He holds a PhD from Imperial College London where he developed machine learning and computer vision methods for continuous facial behaviour analysis, and has contributed to leading open-source projects such as scikit-learn and nilearn. His work spans tensor methods, supervised clustering and neuroimaging analysis—skills honed during roles at Samsung AI Cambridge and internships at Amazon AI and CEA/INRIA. Based in San Francisco, he combines deep theoretical expertise with practical implementation, shipping tested algorithms and examples that improve reproducibility. Colleagues rely on him for robust, well-tested contributions that translate advanced ML concepts into usable tools.
14 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD, Artificial Intelligence, Doctor of Philosophy - PhD, Artificial Intelligence at Imperial College LondonBachelor of Science (BSc), Mathematics, Distinction, Bachelor of Science (BSc), Mathematics, Distinction at Université d'Evry-Val d'EssonneFrench Engineering Diploma, Applied Mathematics, Computer Science and Finance, French Engineering Diploma, Applied Mathematics, Computer Science and Finance at ENSIIE
French, English, Arabic, Spanish
Github Skills (18)
python10
data-science10
clustering-algorithm10
scikit10
mxnet10
machine-learning10
numpy10
deep-learning10
neuroimaging10
scikit-learn10
tensor10
data-analysis10
image-processing9
brain-imaging9
statistics9
Programming languages (6)
Github contributions (5)
scikit-learn/scikit-learn
Jul 2011 - Sep 2015
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:36 commits, 2 PRs, 29 comments in 4 years 3 months
Contributions summary:Jean primarily contributed to the `scikit-learn` repository, focusing on improving the `feature_extraction` module, specifically `grid_to_graph` and `img_to_graph` functions. Their contributions included fixing bugs related to data types and edge cases, such as handling empty edges and ensuring compatibility with boolean masks. They also made code style improvements and added tests to validate the functionality of the image processing graph creation tools.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
nilearn/nilearn
Aug 2011 - Sep 2011
Machine learning for NeuroImaging in Python
Role in this project:Data Scientist
Data ScientistContributions:54 commits in 1 month
Contributions summary:Jean primarily contributed to the development and testing of a module for supervised clustering within the neuroimaging context. Their work involved implementing algorithms and functions for feature agglomeration, parcellation, and signal averaging. The user also wrote tests and examples to validate the supervised clustering methods. The changes encompassed code modifications, test creation, and example simulations related to the core functionality.
neuroimagingpythondecodingbrain-connectivitymachine-learning
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