Shocks and resilience Measuring policy impact in the COVID-19 crisis and building resilience against future shocks
Uncertainty quantification for black box models Developing methods for the quantification of uncertainty in complex numerical models
DECOVID Using detailed, frequently updated health data in a secure database, providing up to date information about patient care during the COVID-19 pandemic
Synthetic population estimation and scenario projection Producing high resolution population projections and a framework for exploring future scenarios
Statistics and the law: Probabilistic modelling of forensic evidence Using real casework to inform the development of probabilistic methods for evaluating complex forensic evidence
Flexible autonomy for swarm robotics Developing the fundamental elements needed for research into the design of large-scale swarm coordination systems that can be flexibly controlled by human operators
Revealing citation cartels in network data Developing methods to detect citation communities and analysing academic citation network data
Performance tuning of systems Investigating how to automate performance tuning of systems, where parameter space is high-dimensional and complex
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
Building a future for the urban analytics blueprint Wednesday 16 Oct 2019 - Friday 18 Oct 2019 Time: 10:00 - 17:00
Machine learning for radio frequency applications How do we generate robust, real-time machine learning algorithms with integrated hardware and software for radio frequency applications?
Uncertainty quantification How do we reliably account for uncertainties when mathematical and computational models are used to describe physical and social processes?