Statistical decision making on non-euclidean spaces

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Context: With the advent of the big data era, data has never been available in such volume and velocity. In parallel, machine learning models are now entering cost sensitive and safety sensitive applications ranging from drug discovery, autonomous driving or medical decision making among others. Having well calibrated measures of uncertainty and risk while decisions are made is paramount for the success and adoption of machine learning tools in these settings. AIMS: The aim of this DPhil project is to exploit the geometry induced by large datasets to improve uncertainty quantification in machine learning models in safety critical tasks. Alignment to EPSRC strategies: This DPhil is well aligned with the Artificial intelligence technologies area of EPSCR's research. Novelty of the research methodology:This research will establish cross-disciplinary links between differential geometry, random manifolds and bayesian machine learning for novel insights. Companies or collaborators involved: This DPhil project is supported by the AWS Studentship in collaboration with the Oxford-Singapore Human Machine Collaboration (HMC) initiative.