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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.
Visualising data profiles and analysis pipelines
Investigating and developing visualisation techniques for profiling data and designing data processing pipelines
Measurement theory for data science and AI
Modelling the skills of learning machines and developing standardised benchmark tests
Design change in digital twins
Developing new methods to visualise the impact of design changes; combining heterogeneous data sources and visualising behaviour of complex systems
Revealing citation cartels in network data
Developing methods to detect citation communities and analysing academic citation network data
Spatial learning for structure-based drug design
Researching how best to represent 3D spatial relationships for machine learning, with a focus on drug design
Language models for quantitative science studies
Developing models to represent the use of language in research and improve performance of specific tasks, including the retrieval and summarisation of literature
Web domain discovery
Developing a scalable way to discover previously unknown government services on the web
Performance tuning of systems
The project investigates how to apply Structured Bayesian Optimisation (SBO) and Deep Reinforcement Learning (DRL) towards automatic performance tuning of systems, where parameter space is high-dimensional and complex
Spatial inequality and the smart city
Identifying and improving observed inequalities in cities, through the investigation of smart city technologies and evolving spatial demographics
Physical activity and cardiovascular health
Understanding the types and patterns of activity that are beneficial for cardiovascular health using machine learning techniques
Personalised risk management of cardiovascular disease
Using cutting-edge statistical and machine learning methods to identify individual cardiovascular risks
Modelling the heart's chemical signals
Obtaining a more complete statistical description of the heart's chemical signals to improve medical treatment
Simplifying the setup of simulations
Developing an intuitive framework to allow researchers to setup, configure and run simulation jobs, using different simulation packages and computing resources
Heart attack risk prediction and treatment management
Using machine learning to predict risk and to inform patient treatment after heart attacks
