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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.
Computational models of meaning change in Ancient Greek
Developing computational models to automatically identify meaning change in the Ancient Greek language
Fundamentals of statistical machine learning
Developing statistical machine learning tools to keep up with the growing needs of the engineering sciences
Innovation to deployment: Machine learning systems design
Improving the way AI is deployed, monitored and maintained through a systems design approach
Intelligent virtual test pyramids for high value manufacturing
Developing novel AI methods to build a more sustainable aviation industry
Improving tracking of iceberg populations in the Southern Ocean
Using machine learning to develop new methods to detect and track icebergs in radar satellite imagery
Enabling trust models for differential privacy
Exploring novel trust models and how much extra utility can be gained from them
AI for precision mental health: Data-driven healthcare solutions
Developing a cost-effective clinical decision support system to help clinicians
Towards incoherent speech as a predictor of psychosis risk
Investigating whether quantitative markers of speech can detect psychotic disorders and help predict clinical outcomes
DeepGalen: Using background knowledge for personalised healthcare
Integrating patient data with genetic, clinical and biological information to aid learning about human disease
Palaeoanalytics
Using data science and machine learning to develop cross-disciplinary analytical methods in human evolutionary studies
Automatic detection of genetic oscillators
Developing data analytics for high-throughput single-cell experimental data to aid better understanding of stem cells
Decision support algorithms for emergency departments
Developing machine learning classifiers to predict departure points in emergency departments in hospitals
Statistics and the law: Probabilistic modelling of forensic evidence
Using real casework to inform the development of probabilistic methods for evaluating complex forensic evidence
