I’m a final year DPhil student at the University of Oxford, in the Department of Statistics, part of the Oxford Statistical Machine Learning Group, supervised by Pr Yee Whye Teh an Ryota Tomioka from Microsoft Research.
My research interests are at the interface of probabilistic machine learning and geometry. Recently I have been exploring the use of machine learning for physical science, and consequently the concepts of symmetry/invariance.
Previously, I was a graduate enrolled in the Mathematiques, Vision, Learning (MVA) master at Ecole Normale Supérieure Paris-Saclay, with an emphasis on machine learning. Prior to that, I worked for Criteo as a data scientist intern, where I improved predictive bidding models. I previously studied at Ecole des Ponts ParisTech in the mathematics and computer science department, where I spent two years.
I am supported by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013) / ERC grant agreement no. 617071, and by Microsoft Research through its PhD Scholarship Programme.
- Dec 2020. I’m co-organizing the first Differential Geometry meets Deep Learning workshop, and we have our paper Riemannian Continuous Normalizing Flows accepted at the Conference on Neural Information Processing Systems (NeurIPS) 2020.
- July 2020. I passed my confirmation of status (last step before the PhD submission).
- Sept 2019. I’m starting an internship at FAIR New York with Max Nickel :)
In our recent work Riemannian Continuous Normalizing Flows, we introduced a model which admits the parametrization of flexible probability measures on smooth manifolds by defining flows as the solution to ordinary differential equations.