Mathematics & Statistics

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Research Themes

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Bayesian machine learning

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Causality-Inspired Machine Learning

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Computational modelling of soft and complex interfaces in flows and bio-inspired phenomena

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Double descent and sufficiency 

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Explainable and scalable statistical models for complex data

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Fluid mechanics

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Gradient boosting for time-to-event analysis

Risk, reliability and machine learning

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Risk evaluation, control and machine learning

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Risk management in renewable energy markets

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Stochastic filtering, numerics, and machine learning

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Stochastic modelling of natural processes