Curriculum Vitae

Last updated on November 2024

For the pdf version of my curriculum vitae please click here.

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.

Professional experience

• Since Feb 2024: Machine Learning Research Scientist
  • Xaira Therapeutics, London.
  • Generative models for protein design.
• Jul 2022 - Feb 2024: Postdoctoral Research Associate
  • University of Cambridge, Department of Engineering.
  • Advisor: Richard Turner and José Miguel Hernández-Lobato.
  • Worked on diffusion models and flow matching for natural science applications.
• 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.

Languages

• French: Mothertongue
• English: Fluent (TOEIC: 930, TOEFL: 103, GRE VR: 157)
• Spanish: Moderate

Computer Skills

• Advanced Knowledge: Python, PyTorch, Bash, LATEX
• Intermediate Knowledge: Julia, TensorFlow, Matlab, C++, JavaScript

Workshop Co-Organization

2020: Differential Geometry meets Deep Learning at Neurips

Reviewing

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