Rémi Lafage
Research Engineer at ONERA - The French Aerospace Lab
Toulouse, Occitania, France
Summary
🤩
Rockstar🎓
Top SchoolRémi Lafage is a research engineer at ONERA with over two decades of experience building simulation and optimization tools for aerospace design and air traffic management. Trained in computer science and applied mathematics at ENSEEIHT, he bridges research and software engineering, leading open-source projects like WhatsOpt, XDSMjs and contributions to linfa and argmin in Rust. His expertise spans surrogate modeling, OpenMDAO integrations, distributed computations and service-oriented web applications, with a track record of turning complex multidisciplinary workflows into collaborative, maintainable tools. Notably, he has steered CleanSky evaluation systems and implemented core optimization/termination logic in high-performance Rust libraries—demonstrating a rare combination of domain knowledge and low-level numerical software craft.
13 years of coding experience
21 years of employment as a software developer
Engineer's degree, Computer Science, Applied Mathematics, Engineer's degree, Computer Science, Applied Mathematics at ENSEEIHT - Ecole Nationale Supérieure d'Electrotechnique, d'Electronique, d'Informatique, d'Hydraulique et des Télécommunications
English
Github Skills (14)
algorithm10
numerical-optimization10
kriging10
algorithms10
machine-learning10
rust10
ndarray10
python10
optimizers10
optimisation10
optimization10
clustering9
optimizer9
state-management9
Programming languages (11)
Github contributions (5)
SMTorg/smt
Aug 2017 - Jan 2023
Surrogate Modeling Toolbox
Role in this project:
Back-end Developer & Machine Learning Engineer
Back-end Developer & Machine Learning EngineerContributions:45 releases, 219 reviews, 422 commits in 5 years 5 months
Contributions summary:Rémi implemented an initial import of functionality from the MOE library. This involved creating a factory for the integration of different models like KPLS, KRG, and related algorithms for surrogate modeling. The commits introduce a new class and adapt interfaces to accommodate the usage of various modeling methods, and integrates new benchmarking problems.
multi-fidelitytoolboxmachine-learningdensity-estimationsurrogate-models
rust-ml/linfa
Nov 2020 - May 2022
A Rust machine learning framework.
Role in this project:ML Engineer
ML EngineerContributions:2 releases, 69 reviews, 10 commits in 1 year 6 months
Contributions summary:Rémi primarily contributed to the development of the `linfa` machine learning framework, focusing on implementing and improving Gaussian Mixture Model (GMM) clustering. They added the GMM algorithm with associated dependencies and features, including error handling, and parameter adjustments. The user also benchmarked the GMM implementation. Further contributions included adding the PLS family of algorithms, the Linnerud dataset, and updating dependencies.
rustscientific-computingmachine-learningframework-learningmachine-learning-framework
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