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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.
Synthetic data and privacy preservation
Exploring the use of synthetic data generators, which offer a naturally private way to generate high-quality data that preserves the statistical features of the original dataset
Economics nowcasting
Building methods to allow rapid assimilation of disparate data in national statistical estimates
Economic networks and transaction data
Developing a UK firm-to-firm payments/monetary flows dataset based on payments systems data to improve quality of economic analysis on key topics for UK policy makers
The Turing Commons
Reflecting, discussing and taking responsibility for the design, development, and deployment of data-driven technologies
ELSA EU Project
Promoting the development and deployment of cutting-edge AI solutions to make Europe the world's lighthouse of AI
Input-output analytics for urban systems
Investigating inter-city trade flows in contemporary and historical contexts
Turing Research and Innovation Cluster in Digital Twins
The Turing and its partners have invested more than £26m in digital twin research and innovation
Dynamic digital twins of interconnected energy and transport networks
Integrating accurate digital representations of existing and planned transport and energy infrastructure
CROP: the Crop Research Observation Platform
A prototype digital twin of the Growing Underground underground farm operated by Zero Carbon Farms Ltd
Computational social science: Social data bias
Investigating social bias in CSS from both a social and computational science perspective
App-based information governance for trustworthy research environments
Delivering an open source 'information governance system in a box' to support data protection in trustworthy research environments
A framework for quantifying the privacy/utility trade-off in generative model based synthetic data
Building a comprehensive picture of privacy and utility for existing generative models
Novel methodologies for assessing and evaluating synthetic data
Aiming to quantitatively and robustly characterise the fidelity, diversity, and generalisation performance of synthetic data
Digital twins for high-value engineering applications (DTHIVE)
Developing digital twins for engineering applications using modelling, data, and connectivity
