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Research projects
Focused scientific study, led by Turing researchers, working with other academics and often industry collaborators, towards defined objectives. Turing research projects aim to be multidisciplinary and multi-institutional, delivering both scientific and real-world impact.
Project Bluebird: An AI system for air traffic control
Advancing probabilistic machine learning to deliver safer, more efficient, and predictable air traffic control
The SPIRIT-AI and CONSORT-AI initiative
Improving the quality of evidence for AI health interventions
Ethical assurance of digital mental healthcare
A participatory approach to providing ethical assurance of data-driven technologies for mental healthcare
Learning machines – infrastructure for augmented intelligence
Developing infrastructure and methods for supporting continuous use of machine learning algorithms
The Alan Turing Institute – Roche strategic partnership
Generating insights into disease, patient, and outcome heterogeneity using advanced analytics
Developing a data charter with Camden Council
A project to co-develop a data charter that can help ensure the responsible use of citizen data
Developing assessment methodology for the evaluation of healthcare AI solutions
Contributing to the development of standards for evaluating AI in clinical studies within the NHS and internationally
Colouring London and the Colouring Cities Research Programme
Developing open source software for platforms able to collect, collate, visualise and release open data on building stocks
Network based recommender systems for public procurement
A novel methodology for community detection and link prediction with applications in public procurement
Ecosystems of digital twins
Bringing together multidisciplinary expertise in urban analytics, complex systems engineering and economics
Impact of climate change on agriculture
Building next-generation models to support resilient agricultural policy
Environment and sustainability
Using data science and AI to tackle environmental and climate challenges
Environmental monitoring: blending satellite and surface data
Intelligent fusion of data from satellite and in-situ surface sensors to help understand our changing planet
Machines reading maps
Creating a generalisable machine learning pipeline to process text on maps and catalysing humanities, scientific, and cultural heritage communities to use map text as data
