The brain is a complex dynamical system presenting multiple challenges from a control theoretic perspective. We will engage with advances in nonlinear, predictive and adaptive control theory as well as the cutting-edge machine learning approaches to decode brain states, modulate brain dynamics and deliver new principles for personalised closed loop control of brain function. We will draw on broad AI expertise applied across domains. Importantly, while computational models can provide a starting point for algorithm optimisation and calibration, the ultimate success of these efforts can only be validated at the interface with live biological circuits. Therefore, success in this WS will require the close collaboration between theoreticians and experimentalists which will be fostered within the network.