I am the Director of the Artificial Intelligence Research Centre (CitAI) and a Reader in Computing at the Department of Computer Science, City, University of London. I study relationships between reinforcement learning and optimization techniques, associative learning, and evolutionary models of aposematism and foraging. I have a keen interest in exploring variational principles and symmetries in learning and behaviour.
More generally, I am interested in applications of AI technology to real-life problems, such as solutions for energy, health, and biochemistry, and in the ethical and legal impact of deep learning, and in investigating whether AI algorithms can produce creative art.
I have published dozens of papers in journals like Neural Computation, Neural Networks, IEEE TNN&LS, and have contributed to The Cambridge Handbook of Artificial Intelligence and several Springer's LNAI and LNCS volumes. My work has been spotlighted by the IEEE Computational Intelligence Society as one of the most important contributions in the area in 2013, and was awarded the First Prize of the European Institute of Innovation and Technology ICT 2014.
I have acted as vice-chair of The Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB), the eldest learned Artificial Intelligence society, and am a member of the EPSRC College.
Webpage at City
I am a lecturer at the Artificial Intelligence Research Centre and a computational cognitive neuroscientist working in nature-inspired AI. My main research focusses on modelling associative learning (AL), and in the conceptual and formal modifications in learning theory that would allow incorporating phenomena that at face value are not susceptible to associative analysis. I have developed several computational learning models such as the Double error Dynamic Asymptote (DDA) model, a fully-connected architecture for Pavlovian conditioning with a dynamic asymptote, which determines the direction of learning, and the Rescorla-Wagner Drift-Diffusion Model (RWDDM) that combines a noisy linear accumulator and the Rescorla-Wagner learning rule.
My current research centres on integrating deep learning architectures and associative learning to explore the possible contribution of associative processes to the formation of representational hierarchies, as a first necessary step towards scaling up to higher-order cognition.
AL is core to bottom-up approaches to natural intelligence and thus keystone to the development of AI algorithms and architectures meant to simulate human-like behaviour.
I have published in presetigious journals such as Psychological Review, Trends in Cognitive Science, PloS Computational Biology, and Science.
I am a senior member of The Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB)
Webpage at City