Johann Faouzi
Assistant Professor at ENSAI
Rennes, Brittany, France
Summary
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Rockstar🎓
Top SchoolJohann Faouzi is an Assistant Professor of Computer Science at ENSAI with ten years of experience at the intersection of data science, machine learning, and time series analysis. He holds advanced training from Ensae ParisTech and Université Paris-Saclay and transitioned from neuroscience research at the Paris Brain Institute to academic teaching and research. Johann is an active open-source contributor, having improved algorithms and robustness in flagship projects like scikit-learn and authored enhancements to the pyts time series package, demonstrating deep practical knowledge of transformations and model stability. His background blends rigorous mathematics, hands-on Python engineering, and attention to code quality and documentation—skills honed through internships in industry and collaborative research. Colleagues describe him as a continuous learner who enjoys translating theoretical ideas into reliable, well-tested software.
10 years of coding experience
2 years of employment as a software developer
Baccalauréat, Mention Très Bien, Baccalauréat, Mention Très Bien at Lycée LakanalMPSI - MP, Mathematics, MPSI - MP, Mathematics at CPGE Lycée LakanalMaster's degree, Data Science, Master's degree, Data Science at Université Paris-SaclayMaster's degree, Data Science - Statistics and Machine Learning, Master's degree, Data Science - Statistics and Machine Learning at Ensae ParisTech
Stackoverflow
81reputation
3kreached
2answers
4questions
Github Skills (25)
algorithm10
pytest10
python10
classification10
testing10
scikit10
machine-learning10
time-series10
classify10
scikit-learn10
documentation10
data-analysis10
data-science9
data-structure9
algorithms9
Programming languages (4)
Github contributions (5)
johannfaouzi/pyts
May 2018 - Dec 2022
A Python package for time series classification
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:10 releases, 13 reviews, 175 commits in 4 years 7 months
Contributions summary:Johann primarily focused on refactoring and improving existing code within the `pyts/transformation` module. Their contributions involved making the code pep8 compliant, correcting syntax errors, and improving docstrings. Furthermore, they implemented and updated several algorithms within this module, showcasing a deep understanding of time series transformation techniques for machine learning applications, and integrated several new algorithms.
pythontime-series-analysismachine-learningtime-series-classificationtime-series
tslearn-team/tslearn
Aug 2019 - Apr 2020
The machine learning toolkit for time series analysis in Python
Role in this project:ML Engineer
ML EngineerContributions:29 commits, 15 PRs, 194 comments in 7 months
Contributions summary:Johann primarily contributed to the testing and documentation aspects of the tslearn library, a machine learning toolkit for time series analysis. They updated tests to filter warnings, remove unnecessary imports, and improve test coverage for the estimators. Additionally, the user added a "contributing" page and improved the integration documentation. Their work demonstrates a focus on code quality, testing, and usability within the context of a time series analysis and machine learning project.
dtwdynamic-time-warpingforecastingpythontime-series-analysis
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