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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.
Identifying urban areas
Building open source tools for better analysing urban boundaries and political fractionalisation
Security and privacy in the decentralised web
Developing techniques to understand and model users and infrastructure in the decentralised web
UK Biobank activity recognition
Using deep learning to find new connections between patterns of physical activity and health outcomes
Geometry and topology of complex interconnected systems
Understanding the structure of complicated datasets to verify hypotheses and predictions about them
Interpreting large datasets using simulation
Developing a sampling method that simulates representative samples given a large dataset
'The Turing Way' - A handbook for reproducible data science
Developing a handbook for best practice in academic data science
From field data to global indicators
Investigating how plant data can be efficiently and reliably linked across data infrastructures around the world
Automated discrimination in internet filtering
Using machine learning to identify discrimination in the choice of websites blocked by internet service providers and government
Women in data science and AI
Using research to inform concrete policy measures aimed at increasing the number of women in data science and AI
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
