Breakthrough mathematics for dynamical systems and data

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This fellowship aims to create a step change in the mathematics we use to learn actionable information from dynamical systems and dynamical data. Using a groundbreaking, operator-theoretic approach to analyse high dimensional systems and spatiotemporal data, this project expects to generate new knowledge in the modelling of complex systems and new pathways for unsupervised machine learning. Expected outcomes of this fellowship include a tranche of new mathematics and practical next-generation algorithms to discover hidden human-understandable patterns in complex dynamical systems and data. This should provide significant universal benefits to many areas of science, including elucidating mechanisms underlying climate and social dynamics.