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Funded research

Coloured particles

Sequential sampling methods for difficult problems

Developing methods to address difficult problems by sampling from the random trajectories of collections of interacting 'particles'

Busy road junction

Nature inspired routing for resilient networked systems

Learning from resilient ecosystems in nature to inform the design of real-time traffic routing and long-term transport infrastructure

Hurricane Maria

A machine learning revolution in disaster response

When a natural disaster strikes, emergency responders and aid agencies need all the intelligence they can get. Turing researchers have combined crowd-sourcing, machine learning and neural networks to rapidly reveal the many dimensions of disasters, and deployed the technology in the aftermath of Hurricane Dorian

Grey London

London air quality

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

Glass lattice roof

Computational statistical inference for engineering and security (CoSInES)

Creating a step change in the use of statistical methodology, motivated by challenges in modelling, computation, and statistical algorithms

London skyline

Robust inference for air quality monitoring

Developing new computational and statistical tools to better understand and predict urban air quality

Pagination

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Related research

Water facility

Critical infrastructures as a control system

Developing smart data communication protocols to achieve robustness and resilience of complex critical infrastructures

Nuclear power station

Data-driven nuclear management

Developing data-driven decision support systems to enable faster, more effective decision making for nuclear engineering operations

Biology lab work

Data-driven experiment design

Developing new statistical tools to select the most informative biological laboratory experiments to perform, to maximise learning and minimise research costs

Explore the Institute

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