*PhD Student in Machine Learning at ENSAE Paris.*

I am a PhD student in the statistics department of CREST, at ENSAE Paris (Institut Polytechnique de Paris) in Paris, France, since September 2018. My advisor is Marco Cuturi.

My research interest lies at the boundary between optimal transport (OT), statistics and machine learning. My work focuses on designing OT tools that are more robust to the curse of dimensionality, to data corruption or noise, so that OT can be efficiently applied to real data problems.

- Our paper
*"Subspace Robust Wasserstein Distances"*is accepted at ICML for poster and 20-minutes oral presentation.

**August 26–31, 2019:**I will participate, and teach a tutorial, in Machine Learning Summer School in Moscow, Russian Federation.

**July 07–19, 2019:**I participated, gave a talk and presented a poster, in Saint-Flour Probability Summer School in Saint-Flour, France.**June 24–28, 2019:**I participated in the workshop People in Optimal Transportation and Applications in Cortona, Italy.**June 09–15, 2019:**I presented (poster and 20-minutes oral) our paper*"Subspace Robust Wasserstein Distances"*at ICML2019 in Long Beach, USA.**March 25–29, 2019:**I presented a poster at the workshop Optimization and Statistical Learning in Les Houches, France.

- F.-P. Paty, A. d'Aspremont, M. Cuturi (2019).
**Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport.***arXiv preprint arXiv:1905.10812.*[ArXiv]

- Paty, F. & Cuturi, M.. (2019).
**Subspace Robust Wasserstein Distances.**Proceedings of the 36th International Conference on Machine Learning, in PMLR 97:5072-5081 [PMLR] [ArXiv] [Github Code] [Poster] [Video]

**July 2019:**I gave a talk at Saint-Flour Probability Summer School.**June 2019:**I gave a 20-minutes oral presentation at ICML2019.

- PhD student at CREST
- Engineering degree from Ecole Polytechnique
- Engineering degree from ENSAE Paris
- Masters degree in Statistics and Machine Learning from Université Paris-Sud

- Geometric Methods in Machine Learning, ENSAE 3rd year students, Spring 2019, Prof.: Marco Cuturi

**---> Jupyter Notebooks** - Stochastic Optimization and Automatic Differentiation for Machine Learning, ENSAE 3rd year students, Spring 2019, Prof.: Marco Cuturi

**---> Jupyter Notebooks** - Optimisation différentiable, ENSAE 1st year students, Spring 2019, Prof.: Guillaume Lecué
- Topologie et Analyse, ENSAE 1st year students, Fall 2018, Prof.: Nicolas Marie