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modeling & environmental risk assessment. Numerical simulation techniques for hydrogeological systems. Advanced uncertainty quantification for robust modeling. Scientific communication, including
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recent large-scale capabilities in physics. Reliability, exploring uncertainty quantification and robust inference in machine learning. Explainability, leveraging identifiability and unique recovery
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) and deep reinforcement learning models Developing novel statistical models for uncertainty quantification, causality estimation, and prediction accuracy Publishing research in leading biomedical and
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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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uncertainty quantification for robust structural design, particularly for complex aero-engine systems with limited experimental data. Recent work by the University of Southampton developed a novel data driven
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the fields of uncertainty quantification, data assimilation and optimisation under uncertainty, complementing data-driven approaches such as physics-informed machine learning. We will start by focusing