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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.
Predicting economic growth from business news
Forecasting the UK's economic growth by analysing the positive or negative sentiment of business news
Network modelling of the UK's urban skill base
Modelling networks of locally embedded knowledge and skills to investigate the future diversification potential of individual UK cities
Letting text speak to economic data
Connecting text-based information to traditional economic measurement systems
Learning tools for analysing land use
Developing the ability to quantitatively understand the complex interplay between the scientific, economic, demographic and other factors that determine land use in the UK
Labour supply in the gig economy
Using machine learning techniques to help better inform policy makers about the constraints and preferences of gig workers
Forecasting with large macroeconomic and financial datasets
Improving the ability of widely used economic forecasting models to deal with volatile changes such as those caused by financial crises
Detecting anomalies in the VAT network
Developing tools to identify abnormal data events (and fraudulent behaviour) in the VAT transactions network
Causal effects in social networks
Better understanding the effects peers have on each other in social networks
Detecting hazardous physical activity
Using human action detection to monitor safety in hazardous or physically demanding situations, such as escalator use and offshore wind turbine work
Extreme event prediction and monitoring
Developing statistical methodology for dealing with extreme events in cyber and transportation systems involving multiple different variables
Detecting anomalous connections on the internet
Building statistical models to measure the likelihood of new connections being formed in a network, to help detect potentially malicious intruders
Spatio-temporal causal inference
Developing methodology for identifying causal connections in settings that have relationships in space and time, such as urban traffic movement
Sequential sampling methods for difficult problems
Developing methods to address difficult problems by sampling from the random trajectories of collections of interacting 'particles'
Computational statistical inference for engineering and security (CoSInES)
Creating a step change in the use of statistical methodology, motivated by challenges in modelling, computation, and statistical algorithms
