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Research Group Overview
This research group focuses on theory and computational aspects of data science approaches to climate and weather modelling. This includes research in uncertainty quantification, data assimilation, probabilistic numerics, multi-fidelity/multi-resolution modelling, ensemble modelling and machine learning for climate.
The research group is a key component of the Met Office Academic Partnership, a cross-department initiative at UCL led by Prof. Serge Guillas.
Research Group Members
| Name | Keywords |
| Francois-Xavier Briol | Bayesian probabilistic numerical methods, Gaussian processes, Machine learning, Uncertainty quantification |
| Alexandros Beskos | Data assimilation, Particle filters |
| Richard Chandler | Climatology, Hydrology, Multimodel ensembles, Downscaling and weather generation, Risk and uncertainty |
| Marc Deisenroth | Data assimilation, Gaussian processes, Machine learning |
| Serge Guillas (Met Office Partnership Lead) | Gaussian processes, Uncertainty quantification (emulation, calibration) of complex computer models |
| Ieva Kazlauskaite (Research Group Lead) | Computational Statistics, General Theory and Methodology, Environmental Statistics |
| Matt Kusner | Causal Inference, Machine Learning |
| Paul Northrop | Extreme Values, Hydrology, Multimodel ensembles |
| Ricardo Silva | Causal Inference, Machine learning |