I am working on Optimal Transport and its applications to structure data in Machine Learning such as Graph Representation Learning, under the supervision of Rémi Flamary and Marco Corneli. Prior to my PhD, I graduated from ENSAE Paris - IP Paris in applied mathematics and machine learning.
I will defend my PhD thesis in early 2023 and I am currently seeking a PostDoctoral position. Please feel free to contact me if you are interested in my research.
You can find my CV here.
- September 2022: Our paper "Template based Graph Neural Network with Optimal Transport Distances" was accepted to Neurips 2022 !
- September 2022: I will attend to GRETSI 2022 and do a long talk on the Semi-Relaxed Gromov-Wasserstein divergence.
- July- August 2022: Enjoying my first go as NeurIPS reviewer.
- July 2022: Glad to attend to CAP-RFIAP 2022 where I will present my work during a talk and poster sessions, plus finally meet more people of the OATMIL ANR.
- June 2022: Let's dive into the community of French statisticians at JDS 2022.
- May 2022: Our new paper Template based Graph Neural Network with Optimal Transport Distances is out on Arxiv (preprint under review)!
- April 2022: Glad to present our paper, Semi-relaxed Gromov-Wasserstein divergence with applications on graphs at ICLR 2022 !