CV

Academic Background

• Temporary Assistant Professor-Researcher (ATER), Université Lyon 1, LIRIS-CNRS, Villeurbanne. From Sept 2025 to Aug 2026.
  • Teaching of Computer Science for a total of 192h eq. TD. Courses: introduction to programming and algorithmics, data structures, web development and networks in at the License levels. Data analysis and machine learning (introduction, applications to computer vision) at the Master levels.
  • Research in Reinforcement Learning around exploration and task transfer, within the SyCoSMA team of LIRIS and in collaboration with Laetitia Matignon and Mathieu Lefort.
• Post-doctorate in Sustainable AI, Imec, Antwerp, Belgium. From Nov 2023 to Nov 2024.
  Specification of the imec mission on “Sustainable AI for Industrial Process Control”, mainly centered around applications in the semi-conductor industry.
  • Redaction of research project proposals for PFAS detection, optic metrology, GHG emission reduction…
  • Discussions and collaborations with the Imec teams to evaluate current state of practice.
  • Presentation of propositions to decision-makers.

• PhD. in Computer Science, University of Antwerp - Imec, IDLab, Antwerp, Belgium. From Oct 2019 to Oct 2023.
  4-year doctoral contract at IDLab Antwerp under supervision of Kevin Mets and Steven Latré. Theoretical research in deep reinforcement learning on task transfer, intrinsic motivation and unsupervised skill discovery.

• Research Engineer Internship, EDF, Chatou, France. From March to Aug 2019.
  Development of algorithms for the optimisation of the layout of a nuclear reactor. Implementation of evolutionary algorithms to optimize the weights of a deep neural network.

• Engineer in Applied Mathematics, INSA Rouen, France.
  General engineering knowledge in the first two years, specialization in Applied Mathematics in the last three. Exchange at Bishop’s University (Canada) for Machine Learning courses; final project on deep reinforcement learning.

PhD in Computer Science

Thesis titled Transfer and Zero-shot Reinforcement Learning: Learning Behaviors Without a Reward Function.

PhD under the supervision of Kevin Mets and Steven Latré at IDLab at the University of Antwerp, Belgium, under funding of the Flanders AI Research Program. Public defense held on December 12th, 2025. Study of unsupervised skill discovery and task transfer in reinforcement learning: learning behaviors and representations without reward to quickly maximize any downstream task reward. The first half of the doctorate focused on exploration, intrinsic motivation and hierarchical reinforcement learning, while the second half focused on unsupervised skill learning and zero-shot task transfer. Research activites: conception and development of algorithms for deep reinforcement learning, experimentation and evaluation in simulated environments (robotics, video-games) and publication of results in scientific conferences and journals.