Projects

Adaptive MCMC

Optimising sampling algorithms used for genomics, infectious diseases, climate, and financial and industrial models.
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Clean air in London

Developing machine learning algorithms and data science platforms to understand and improve air quality over London.
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Inverse problems

Developing statistical tools for accurate quantification of uncertainty in important applications, such as medical imaging and industrial safety.
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Large transport systems

Developing models of passenger movement and reaction to closures, to learn how usage fluctuates in systems like the London Underground.
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Disaster management

Developing novel machine learning approaches to data fusion, to aid with disaster management policy and response.
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Probabilistic numerics

Providing engineers with tools to mitigate the 'numerical risk' associated with unreliable calculations based on physical models.
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CHANCE

Using network science to tackle the large uncertainties caused by chain reactions in critical infrastructure systems.
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Security in the cloud

Investigating and prototyping the secure software components needed to enable data sharing without compromising data privacy.
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