Research themes
Research here focuses on automated science: uncertainty-aware deep learning, colour and qualia drift, embodied motor control, and neural plasticity work that feeds autonomous discovery loops. These themes develop the models, evaluation protocols, and idea-generation rituals that our systems run.
Uncertainty in Deep Learning
Calibrated deep learning through adaptive regularisation, online resampling, and geometry-aware Bayesian posteriors.
Colour, Consciousness & Qualia Drift
Empiricist theories of qualia paired with six-fundamental colour experiments, agent diaries, and installations that let people feel new spectra.
Motor Control & Embodied RL
Hierarchical world models, cerebellar-inspired controllers, and perturbation studies that push adaptive behaviour in robots and virtual agents.
Neural Plasticity & Representation
Theory and experiments on how synapses and adaptation rules learn structure across cortex and motor circuits.