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
How can we strategically allocate limited research resources to maximize scientific progress? Can citation network analysis reveal the structural gaps that, when filled, accelerate knowledge creation and collaboration across disciplines?
Can machines truly reciprocate human emotional bonds, or does anthropomorphism merely project meaning onto algorithmic responses? As AI becomes increasingly humanlike, how do we navigate consent, attachment, and the ethics of designing companions without consciousness?
How do human cognitive biases and commercial incentives shape the supposedly "objective" algorithms that increasingly govern our attention, decisions, and social institutions—and can transparency offer a path toward accountability?
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.