Advancing preference learning, optimisation theory, and scalable human centric AI systems for complex decision environments.
We push the boundaries of how intelligent agents model human intent and navigate uncertainty. Our research translates strong theoretical foundations into actionable architectures.
Structured modelling of human values and trade-offs in complex multi-objective environments.
Robust decision frameworks for high-dimensional, partially observed systems.
Interactive architectures that combine algorithmic precision with expert oversight.