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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.
Molecular causal networks of hypertension
Using machine learning techniques to investigate causal networks of hypertension at molecular level
Personalising treatment in type 2 diabetes
Developing and testing a decision support tool for primary care to optimise the selection of glucose-lowering therapy for people with type 2 diabetes
Metabolomics networks for medicines testing
Using time dependent and genetically informed causal metabolomics networks for the assessment of medications
Virtualised hardware acceleration for data analytics
Improving performance, efficiency and flexibility of analytics systems
Project FAIR: Framework for responsible adoption of artificial intelligence in the financial services industry
Responsible artificial intelligence in financial services
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
The Online Harms Observatory
Providing real-time insight into the scope, prevalence and dynamics of online harms
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
