Hi! I’m a research scientist in machine learning at the research center of Safran, a french company that designs and manufactures various aerospace and defense equipments. I specialize in machine learning on graph data and I have a strong interest in uncertainty quantification. Developing scientific codes is also an area that I particularly enjoy.

In 2018, I defended my PhD thesis on uncertainty quantification in computational mechanics. Since then, I have been working as a machine learning researcher with a focus on applying my expertise to the field of aeronautics. I spend my time working on Bayesian inference and conformal predictions for uncertainty quantification, as well as kernel methods and deep learning for surrogate modeling.

Research interests

• Uncertainty quantification in deep learning (Bayesian neural networks, ensemble techniques, conformal predictions)
• Kernel methods for post-processing MCMC outputs
• Machine learning for graphs (graph neural networks, graph kernels, optimal transport, …)
• Stochastic modeling in computational mechanics