Learn more here!
Ward is an FWO PhD Fellow studying compositionality as a mathematical foundation for deep learning. He uses tensor networks and category theory to analyse neural representations as induced by the computations encoded in a model’s weight structure.
Instead of reading tea leaves in activation space, he treats the model’s weights as a formal compositional system, where local mechanisms compose into global properties. Emergent behaviour then becomes a direct function of the algebraic wiring, and the black box divides into parts small enough to conquer.
His work proposes tensor models to bridge neuro-symbolic AI and mechanistic interpretability, asking how models compose concepts to generalise… or not.