Vincent Dubourg
Wear Performance Analyst at Michelin
Greater Clermont-Ferrand Area France
Summary
👤
Senior🎓
Top SchoolVincent Dubourg is a Pi-shaped data scientist and wear performance analyst with 15 years of experience applying probabilistic design, surrogate modelling and reliability methods to mechanical engineering problems. He holds a PhD in Mechanical Engineering and has progressed from research and teaching roles to senior data science positions at Michelin, where he now focuses on wear performance and practical, uncertainty-aware analytics. Vincent combines deep applied maths and finite-element intuition with hands-on open-source work—his scikit-learn contributions include implementing a Kriging/Gaussian Process model and a probabilistic classification example. He favors principled data science over hype, emphasizing model validity and uncertainty quantification in industrial applications. Based in the Clermont-Ferrand area, he also regularly delivers training for engineers, bridging academic rigor and production-ready solutions.
15 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Mechanical Engineering, Doctor of Philosophy (Ph.D.), Mechanical Engineering at Université Blaise Pascal (Clermont-II) - Clermont-FerrandEngineering diploma, Structural mechanics, optimization & reliability, Engineering diploma, Structural mechanics, optimization & reliability at Institut français de Mécanique avancée
French, English
Github Skills (9)
gaussian-processes10
scikit-learn10
machine-learning10
regression-models10
python10
scikit10
data-science9
data-analysis9
statistics9
Programming languages (7)
Github contributions (5)
scikit-learn/scikit-learn
Nov 2010 - Dec 2013
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:39 commits in 3 years 1 month
Contributions summary:Vincent's primary contribution centers around implementing a Kriging model class, a Gaussian Process-based prediction method, within the scikit-learn project. This involved creating the `kriging.py` file, integrating it into the main `__init__.py` module, and defining key functionalities for both regression and probabilistic classification tasks. Subsequent commits focused on bug fixes related to parameter handling and incorporating a Gaussian Process model. The user also introduced a probabilistic classification example utilizing the GaussianProcessModel, demonstrating its capabilities.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
dubourg/python-randomfields
Mar 2013 - Jun 2016
Contributions:5 commits, 2 PRs, 2 pushes in 3 years 3 months
representationpythonkarhunen-loeverandom-fieldssimulation
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