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Who am I ?

PhD in Applied Mathematics, I am a Maître de Conférences (~associate professor) since September 2025 in the GMI department of Institut Henri Fayol which is part of Mines Saint-Etienne.

My research interests are (but not limited to)

• Uncertainty Quantification
• (Multiobjective) Optimization Under Uncertainties
• Machine Learning
• Data Assimilation
• Dimension Reduction
• Calibration of numerical models

CV and Resume

• Academic CV

Teaching

I taught some courses at UGA, ECL and EMSE. A detailed breakdown can be found. As of now, I am mostly giving lectures in the Statistics and Data Science course (Core Curriculum), Data Science Major, and the Operational Research and Decision Support Tools toolbox

• List of teachings

Previous positions

• From April 2024 to April 2025, I held a postdoctoral position at ICJ, hosted at École Centrale de Lyon, where I worked on Multi-Objective Optimization under Uncertainties using Bayesian Optimization.
  • Multiobjective Optimization under uncertainties using Conditional Pareto Fronts (Preprint)
• From December 2021 to December 2023, I was a postdoctoral researcher in the joint laboratory between AI4Sim (Github public repo, which is part of Eviden R&D department, and AIRSEA (Inria research team). In this postdoc, I worked on
  • Building preconditioners for Variational Data Assimilation using Machine Learning (Preprint)
  • Non-Linear Dimension reduction
• From October 2017 to July 2021, I was a PhD student in AIRSEA (Inria research team), working on the calibration of a regional model of the ocean under uncertainties.

Relevant links

Publications and PhD Dissertation

• PhD Dissertation: full-text, web-page (defended on June, 11th 2021)
• Publications
• Orcid / HAL

Science Dissemination

• In a science popularization purpose, a short presentation I made for high-school interns about Optimization and Multiobjective Optimization (in french).
• My former team made a video explaining the modelling of the ocean (in french)

Repositories

• moouu: Multiobjective Optimization under uncertainties using Bayesian Optimization (soon public)
• eplus_wrapper: Python wrapper and containerization of EnergyPlus for optimization and uncertainty quantification on buildings.

Not really maintained repos

• robustGP: Python package to construct and run Adaptive design methods based on Gaussian Processes for optimization under uncertainty
• ML_preconditioners: Use ML to construct and test preconditioners in Variational Data Assimilation
• DA_PoC: Library to test and prototype quickly Data Assimilation procedures