CV

PhD in Machine Learning

• Under the supervision of Alain Durmus.
• Generative modelling, sampling, Schrödinger Bridge, dynamic optimal transport, stochastic optimal control, diffusion models, Markov Chain Monte Carlo methods.
• Papers
  • 2 NeuRIPS papers
  • 1 ICML paper
  • 1 COLT paper

MRes. in Mathematics, Vision and Learning ("MVA")

• Distinctions with jury’s congratulations.
• Generative modelling, Bayesian Machine Learning, Computational statistics, Optimal Transport, Probabilistic Graphical models.

MSc. in Applied Mathematics ("Ingénieur Polytechnicien")

• GPA 3.93/4, top 10% of all students.
• Statistics, Optimization, Advanced Machine learning, Statistical modelling, Monte Carlo simulations, Stochastic calculus.

Research Intern

• Under the supervision of Valentin de Bortoli.
• Worked on sampling algorithms for constrained spaces with self-concordant barriers.

Research Intern

• Under the supervision of Aymeric Dieuleveut and Aurélien Bellet.
• Worked on federated learning comprising concerns about privacy and data heterogeneity.

Data Scientist Intern

• Worked with the R&D team on improving data completeness in the collection and use of energy consumption invoices (scraping & parsing).

• Awarded a selective PhD scholarship by Ecole Polytechnique.

• Awarded the Ecole Polytechnique Research Centre Prize in Applied Mathematics.

• NeurIPS

• AISTATS, ICML, NeurIPS (Top Reviewer), UAI, TMLR

• ICML (Top Reviewer), NeurIPS, TMLR

• ICLR