Ontology-grounded LLM systems
Combining domain ontologies, knowledge bases, and language models for grounded reasoning and explanation.
Applied AI for knowledge-intensive domains
I design ontology-grounded and agentic AI systems that combine LLMs, knowledge graphs, and explainable recommendation for skill intelligence and personalized learning.
My recent work focuses on grounded AI systems for education, skill management, vehicle recommendation, and semantic search. I am interested in systems that can explain their recommendations, respect domain knowledge, and adapt to user context.
I previously held a postdoctoral position at Heudiasyc, Université de Technologie de Compiègne. I earned my PhD at IMT Atlantique, where my doctoral research explored semantic representation for French with CCG and DRT.
Combining domain ontologies, knowledge bases, and language models for grounded reasoning and explanation.
Tool-using AI workflows for skill intelligence, learner guidance, and human-in-the-loop decision support.
Semantic representations that make recommendation, search, and personalization more transparent.
Competency modeling, training recommendation, and learning trace analysis for personalized progression.
Competency modeling, grounded skill search, explainable recommendations, and personalized training recommendation.
A web-first task workspace where each task keeps context, connected notes, and a small memory layer.
Ontologies, RDF similarity, user profiles, and context-aware recommendation for used-vehicle search.