PhD candidate in graph machine learning at the Vrije Universiteit Amsterdam under the supervision of Dr. Jieying Cheng and Pr. Michael Cochez, with Pr. Stefan Scholbach as advisor. Part of the KAI research group. Guest in the L&R group. Half funded by the Zorro project. A consortium with ASML, Canon Production Printers, ITEC, Philips, and ThermoFisher Scientific.
Officially advancing explainability of neuro-symbolic AI. Tactically working on mechanistic interpretability for graph ml. Personally trying to bridge disciplinaries I love, networking philosophy of mind, social sciences, AI and neurosciences through the lens of graph theory and XAI. Systematizing rhizomes.
Interactive CV Network
Disclaimer : This text was co-written with an LLM. I mention it both out of formal transparency, and because the article below raises precisely the question of the relation between the subject and the apparatus that models him — and it seemed dishonest not to signal that the "I" of this post is itself a relation, not a sovereign subject.
Bienvenue en Touraine, pays de Balzac, de Léonard de Vinci, des châteaux de la Loire, du saint-nicolas-de-bourgueil, du chinon et du vouvray.
Tom Pelletreau-Duris, Ruud van Bakel, Michael Cochez — Proceedings of The 19th International Conference on Neurosymbolic Learning and Reasoning ·
Presents a model-agnostic explainability pipeline that uses diagnostic classifiers to probe graph neural network embeddings for structural graph properties, clarifying what information their hidden states retain across architectures and datasets.