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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.
Multi-level modelling, simulation and visualisation
Integrating population growth projections, agent-based modelling and interactive traffic simulation to assist the development of intelligent transport systems
Wind behaviour prediction for protection of infrastructure
Predicting and warning extreme wind behaviour to improve public safety and protect key infrastructure
Interpreting spatial flow data
Exploring methods of spatial data visualization for better perception of flow, with application to big data from the UK
Posterior bootstrap: A scalable approach to Bayesian non-parametric learning
Providing an open-source tool to make non-parametric statistical inference faster and more accurate
Citizen science platform with Autistica
Building a participatory, citizen-science platform investigating sensory processing and autism
Digital twins in aeronautics
Developing next generation digital twins using advances in aerodynamics, applied mathematics and computational statistics
Theoretical foundations of engineering digital twins
Developing theoretical foundations underpinning digital twins for complex engineering systems
Actionable insights from free text police data
Using topic modelling to identify specific crime modus operandi from police free text data
A common interface for discrete choice
Assessing the viability of translating the ALOGIT software to Python and benchmarking it against other discrete choice modelling software
Model criticism in multi-agent systems
Enabling autonomous agents to reason about the adequacy of models used to predict the actions of
other agents
Double the trouble: gene regulation in polyploids
Predicting the gene expression patterns in brassicas - the plant group that includes broccoli, cabbage and cauliflower
New data forms for transport policies
Using new types of digital data to support more sustainable travel choices, reducing health, energy, security and other impacts to improve urban lifestyles
Physics-informed statistical learning of battery lifetime
Developing physics-informed machine learning of dynamical systems with applications to battery degradation
Decision-making under uncertainty in air traffic control
Investigating machine learning methods to support air traffic controllers
