Antoine Grosnit is a research engineer with 8 years of experience bridging applied mathematics and machine learning, currently working at Meta after five years on Huawei’s optimisation team. He has a strong academic foundation from École Polytechnique and ENS Paris-Saclay (MVA) and a track record of publishing and implementing provably convergent RL algorithms from internships at IBM. His contributions to open-source Bayesian optimization (adding the CompBO module and BOiLS to Huawei Noah’s HEBO repo) reflect hands-on experience turning research ideas into reusable libraries. He combines theoretical expertise in optimisation and multi-agent RL with practical engineering—building algorithms, integrations and efficient graph tools in production contexts. Comfortable in research and product environments, he also brings teaching and assessment experience from lecturing and examining mathematics at university level. A former Air Force communications officer, he pairs analytical depth with disciplined program coordination and outreach.
8 years of coding experience
6 years of employment as a software developer
Bachelor’s degree, Philosophy, Bachelor’s degree, Philosophy at Université Paris Nanterre
Master's degree, Mathematics, Vision, Learning (MVA), with Highest Honors, Master's degree, Mathematics, Vision, Learning (MVA), with Highest Honors at École normale supérieure Paris-Saclay
Applied Mathematics, Applied Mathematics at École Polytechnique
Classe Préparatoire aux grandes Ecoles (Undergraduate Studies), filière MPSI - MP*, Mathematics and Computer Science, Classe Préparatoire aux grandes Ecoles (Undergraduate Studies), filière MPSI - MP*, Mathematics and Computer Science at Lycée Louis-le-Grand
Bayesian optimisation & Reinforcement Learning library developed by Huawei Noah's Ark Lab
Role in this project:
ML Engineer
Contributions:1 release, 17 reviews, 53 commits in 1 year 3 months
Contributions summary:Antoine implemented and added the CompBO module, indicating work on Bayesian optimization and reinforcement learning, central to the repository's purpose. The commit involves modifications to the `bayes_opt.py` file, likely adding functionality or updating existing Bayesian optimization algorithms. The user also integrated new components such as BOiLS, extending the library's capabilities.
Contributions:63 commits, 69 pushes, 1 branch in 1 year 2 months
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