— Define innovation roadmap and lead state-of-the-art reviews.— Design and develop solutions using ML on structured data (GNN, knowledge graphs, NLP) and integrate LLMs/GenAI for product enhancement.— Manage technical architecture, estimate resources, and maintain a scientific and technological watch.

Learner modeling, interpretable machine learning & cognitive sciencePh.D. conducted on a cross-disciplinary topic bridging machine learning and cognitive science: "Adapting the Prerequisite Structure to the Learner in Student Modeling" (available online).— I designed explainable machine learning models to predict learner knowledge and learner performance.— I developed an open-source framework to design an intelligent tutoring system based on these models.— I focused on machine learning interpretability by defining new metrics for educational systems' users.— I did national and international communications: RJC-EIAH 2020 (best paper award), EIAH 2021, EDM 2023.

Research, project management & product conception— I designed the Kartable website's recommender system and the structure of pedagogical resources with advanced ML techniques to turn it into an explainable intelligent tutoring system.— I led the pedagogical resources production with a diverse editorial team (teachers, editor, graphist, proofreader).— I designed tools to optimize editorial team processes from storymap to proof of concept.— I did scientific lookout of existing adaptive learning solutions.— I reported my work to the entire team, advocating for the benefits of artificial intelligence for the company.

5-month research internship in the Atos Quantum team on trapped-ions quantum computer.— theoretical work on quantum information— numerical simulations — signal processing

3-month research internship in the department of Condensed Matter Physics.— atomic and magnetic force microscopy on quantum materials— physical numeric simulations