Life, learning and emergence
What is life, and why is artificial life so elusive? How can we characterise and augment machine learning? We seek a fundamental understanding of life, learning and other emergent phenomena, and we create systems for automated decision-making, inference and discovery.
Developing radical new approaches to inference and automated decision making using advances in quantum information and statistical physics.
Developing a new approach to resilience in which mistakes and unexpected events are mitigated by easy repairs rather than redundancy.
Applying mathematical tools to the holy grail of cellular biology: can we produce every type of human cell from within the laboratory?
Understanding complex dynamical behaviours generated by simple rules, such as cellular automata, polyominoes and models of competition.
Predicting the behaviour of graphs and processes on them by treating topological patterns as constraints on a random graph ensemble.
Creating mathematical tools for characterizing the structure of ideal graphs and irregular networks, and the behaviour of processes on them.