*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.

I am one of the organizers of the seminar Stat·Eco·ML.

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.

**July 12–18, 2020:**I will present our paper*"Regularized Optimal Transport is Ground Cost Adversarial"*at ICML 2020 online.**August 26–28, 2020:**I will present our paper*"Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport"*at AISTATS 2020 online.

**January 17, 2020:**I gave a talk (.key, .pdf) at the seminar day Learning meets Astrophysics at CEA in Saclay, France.**December 08–14, 2019:**I presented our paper*Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials*at NeurIPS Optimal Transport and Machine Learning Workshop in Vancouver, Canada.**November 20, 2019:**I gave an introductory lecture on optimal transport at the seminar Stat·Eco·ML in Palaiseau, France.**November 05, 2019:**I gave a talk (.key, .pdf) at Le Séminaire Palaisien in Palaiseau, France.**August 26–31, 2019:**I participated in, and gave a tutorial (notebooks, video) at Machine Learning Summer School in Moscow, Russian Federation.**July 07–19, 2019:**I participated in, gave a talk (.pdf) and presented a poster (.pdf), 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-minute oral) our paper*"Subspace Robust Wasserstein Distances"*at ICML 2019 in Long Beach, USA.**March 25–29, 2019:**I presented a poster (.pdf) at the workshop Optimization and Statistical Learning in Les Houches, France.

- F-P. Paty, M. Cuturi.
**Regularized Optimal Transport is Ground Cost Adversarial.***ICML 2020.*[ArXiv] [Slides] - F-P. Paty, A. d'Aspremont, M. Cuturi.
**Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport.***AISTATS 2020.*[PMLR][ArXiv] [Slides] - F-P. Paty, M. Cuturi.
**Subspace Robust Wasserstein Distances.***ICML 2019.*[PMLR] [ArXiv] [Github Code] [Poster] [20-minute oral video]

**January 2020:**I gave a talk (.key, .pdf) at the seminar day Learning meets Astrophysics.**November 2019:**I gave a talk (on the blackboard) at the seminar Stat·Eco·ML.**November 2019:**I gave a talk (.key, .pdf) at Le Séminaire Palaisien.**August 2019:**I gave a tutorial (notebooks, video) at MLSS 2019.**July 2019:**I gave a talk (.pdf) at Saint-Flour Probability Summer School.**June 2019:**I gave a 20-minute oral presentation (video) at ICML.

- 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

- Optimal Transport: Theory, Computations, Statistics, and ML Applications, ENSAE 3rd year students, Spring 2020, Prof.: Marco Cuturi
- Deep Learning: Models and Optimization, ENSAE 3rd year students, Spring 2020, Prof.: Marco Cuturi
- Optimisation différentiable, ENSAE 1st year students, Spring 2020, Prof.: Guillaume Lecué
- Topologie et Analyse, ENSAE 1st year students, Fall 2019, Prof.: Nicolas Marie

- 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