I am currently a PhD candidate at LAMSADE, a joint research lab of Université Paris-Dauphine and Université PSL, specializing in Generative Models.

My initial research was centered around Adversarial Attacks generated using Invertible Neural Networks. However, mu current work focuses on the expressivity of Generative Models and the trade-off between the quality and the diversity of the generated data. By properly choosing the divergence that models are trained to minimize, the overall behavior can be set in advance, and thus I have worked on tuning and improving models such as Normalizing Flows, Generative Adversarial Networks or Diffusion Models.

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  • Deep Learning
  • Generative Modelling
  • Precision and Recall for Generative Models
  • PhD in Artificial Intelligence, 2023

    Université Paris-Dauphine

  • Multidisciplinary Master's Year in Quantitative Economics, 2020

    Université Paris-Dauphine

  • MVA - MEng in Computer Science, 2019

    École Normale Supérieure Paris-Saclay

  • Multidisciplinary Master's Year in Fundamental Physics, 2018

    Université Paris-Sud

  • BSc in Electrical Engineering, 2017

    École Normale Supérieure Paris-Saclay

Recent Publications

(2024). Exploring Precision and Recall to assess the quality and diversity of LLMs. In ACL 2024 (Main).

PDF Cite arXiv Google Scholar

(2024). Optimal Budgeted Rejection Sampling for Generative Models. In AISTATS 2024.

PDF Cite arXiv Google Scholar

(2023). Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows. In NeurIPS 2023.

PDF Cite arXiv Google Scholar

(2022). On the expressivity of bi-Lipschitz normalizing flows. In ACML 2022.

PDF Cite arXiv Google Scholar

(2021). On the expressivity of bi-Lipschitz normalizing flows. In INNF+ 2021.

PDF Cite arXiv OpenReview


Current Courses

Hands-On IAEMWe AreLectures2024
Introduction to Deep LearningDLE.M.Lectures2023-2024

Past Courses

Deep Learning ProjectDLPM.S.Lectures2022-2023
Mathematics for Data ScienceMSDM.S.Lectures2020-2023
Projet IAIAE.M.Lectures2022-2023
Artificial IntelligenceIAM.S.Seminars2021
Introduction to Normalizing FlowAM.S.Lectures2021
Information System EngineeringISI1B.S.Lectures/Seminars2020


LAMSADE - Université Paris-Dauphine
Sep 2019 – Present Paris
PhD Candidate at LAMSADE working on the expressivity of generative models.
LAMSADE - Université Paris-Dauphine
Research Intern
Sep 2019 – Jun 2020 Paris
Part-Time Research internship on generation of Adversarial Attacks with Invertible Neural Networks.
Research Intern
Apr 2019 – Sep 2019 Paris
Master’s degree research internship on Invertible Neural Networks as a defense against Adversarial Attacks.
Advanced Structures & Composites Center - University of Maine
Research Intern
May 2018 – May 2018 Orono, Maine
Master’s degree research internship in Organic Photovoltaic Materials Through an Educational Partnership Agreement between the University of Maine and the US Army.