Digital twins for high-value engineering applications (DTHIVE) Developing digital twins for engineering applications using modelling, data, and connectivity
Quantitative Urban ANalyTics (QUANT) A land use transportation model simulating the location of employment, population and transport interactions
The eye: a window into brain cardiovascular disease Uncovering retinal microvascular predictors of compromised brain haemodynamics in small vessel disease
Shocks and resilience Measuring policy impact in the COVID-19 crisis and building resilience against future shocks
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
Pinpointing molecular mechanisms of complex disease Combining population and molecular data to pinpoint disease causing DNA changes
Uncertainty quantification of multi-scale and multi-physics computer models Developing new tools to investigate and quantify uncertainties in computer models, with applications to climate, earthquake and tsunami models
Detecting anomalies in the VAT network Developing tools to identify abnormal data events (and fraudulent behaviour) in the VAT transactions network
Optimal execution strategy with an uncertain volume target In the seminal paper on optimal execution of portfolio transactions, Almgren and Chriss (2001)... Vaes, J. and Hauser, R. (2018). Optimal execution strategy with an uncertain volume target. arXiv:1810.11454 [q-fin.TR].
Reliability and reproducibility in computational science Friday 24 Jan 2020 Time: 09:30 - 17:00 Peter V. Coveney Dan Crommelin Onnie Luk Nick Malleson Anna Nikishova
Mathematics and data Thursday 12 Sep 2019 Time: 10:00 - 18:00 Alexander Gorban Felipe Rincon Ginestra Bianconi John Oprea Nati Linial Shay Moran
Data science for engineering structural integrity How can data science be used to improve the assessment of structural integrity across engineering sectors?
AI in resource- and data-constrained environments How can we build effective predictive models on hardware platforms that have limited computational resources and/or when the model training data are by definition always limited in amount?
Machine learning and dynamical systems How do we analyse dynamical systems on the basis of observed data, rather than attempt to study them analytically?