As anthropogenic warming continues to put strain on our natural environments, it now is crucial that decision-makers have ready-access to actionable and interpretable information on the current states of environmental systems and future projections, with quantifiable uncertainties, to help safeguard human populations and delicate ecosystems.

Digital Twinning is a next generation technology for environmental modelling, taking us beyond data assimilation by enabling us to get answers to “what-if” questions within seconds. Digital Twins are already in operation in industry and involve highly interoperable data pipelines, optimisation, and a mixture of knowledge-informed and data-driven probabilistic machine learning. Digital Twins are increasingly becoming a key component for research discovery, education, and aiding discussions at the board-level without having to wait weeks to months for results from traditional computer models.

IceNet: AI for seasonal sea ice forecasting

Using machine learning techniques to predict and understand the complex drivers of sea ice

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AI for Environmental Monitoring

Blending satellite, surface and simulation data

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Tracking iceberg populations

Using machine learning to develop new methods to detect and track icebergs in radar satellite imagery

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AI and Digital Twinning for Decarbonisation

AI and Digital Twinning to achieve carbon reduction on-board RRS Sir David Attenborough

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Funding

I have secured over £10m in grant funding from public and private sectors as both Principal-Investigator (PI) and Co-Investigator (Co-I). These include: