Curriculum Vitae

Last updated on 07/06/2023

Education

2017 - 2021: PhD in Machine Learning with Prof. Yee Whye Teh at University of Oxford, Department of Statistics.
2016 - 2017: MSc (II) in Machine Learning & Computer Vision (MVA) at Ecole Normale Supérieure Paris-Saclay, Paris, passed with honours, Gpa: 4/4.
2014 - 2015: MSc (I) in Mathematics & Computer Science at École des Ponts ParisTech, Paris, Gpa: 3.94/4.
2013 - 2014: BSc in Science (Mathematics, Physics and Computer Science), at École des Ponts ParisTech, Paris, Gpa: 3.86/4.
2011 - 2013: Preparatory classes in Mathematics and Physics, PSI*, Nantes.
2011: Baccalauréat (French secondary school diploma), Nantes, Science major, Mathematics option, passed with honours.

Since Jul 2022: Postdoctoral Research Associate
- University of Cambridge, Department of Engineering.
- Advisor: Richard Turner and José Miguel Hernández-Lobato.
Aug 2021 - Jul 2022: EPSRC Postdoctoral Research Associate
- University of Oxford, Mathematical Institute and Department of Statistics.
- Advisor: Arnaud Doucet.
Autumn 2019: Research Intern
- Facebook Artificial Intelligence Research, New York.
- Worked on extending normalizing flows to manifolds (1), with climate science application.
- Supervisor: Maximilian Nickel.
Summer 2017: Research Intern
- Department of Statistics, University of Oxford.
- Studied sampling methods for discrete random probability measures in probabilistic programs. Contributed to the open source probabilistic program Turing.jl.
- Supervisor: Yee Whye Teh.
Summer 2016: Machine Learning Intern
- Criteo Lab, Paris
- In the context of online auctions, improved predictive bidding models accuracy in the presence of perturbative and periodical events such as sales.
- Supervisor: Regis Vert / Patrick de Pas.
Autumn 2015: Software Engineer Intern
- BAM Lab, Paris.
- Worked as a full-stack developer, using leading technologies to develop mobile and web- site applications, and their associated backend services.
Summer 2014: Data Scientist Intern
- IFSTTAR Research Institute, Paris.
- Applied unsupervised probabilistic models such as LDA, to transportation’s data in order to better understand commuters behaviour.
- Supervisor: Etienne Come.

2022: ICLR (Highlighted Reviewer) 2021: NeurIPS, DGMs @ NeurIPS 2020: NeurIPS, AABI 2019: ICML, NeurIPS 2018: AABI, BDL @ NeurIPS