Incredible technological advances in data collection and storage have created a world in which we are constantly generating data. From supermarket loyalty cards and social media posts to healthcare records and credit card transactions, a digital footprint exists for every aspect of our lives. The ability of data science to analyse and act upon these complex and varied data sources has the potential to improve and revolutionise our lives in a myriad of ways, for example, through the development of driverless cars and personalised medicine. The great challenge of data science lies in the trade-off between the speed and accuracy with which large volumes of data can be analysed and acted upon within complex data environments. Extracting deeper knowledge from data requires increasingly sophisticated mathematical models. However, applying such models introduces significant computational constraints, forcing data scientists to rely upon simpler models or approximate inference tools. In collaboration with strategic partners, this project will bring together industry experts to investigate new approaches to data science driven by fundamental challenges in modelling and analysing large-scale spatial and security data. The data and issues within this domain are highly-significant to modern society as they cover, for example, issues pertaining to fraud detection and computer hacking, as well as understanding and predicting human behaviour within a Smart City environment. Novel mathematical advances in computational statistics and machine learning will be developed to produce scalable techniques for applying sophisticated mathematical models to large-scale heterogeneous and structured data sources. A key component of this project is reproducibility through the creation of open-source software. These tools will allow data scientists to implement research outcomes to extract key features from complex data and make decisions with high accuracy under uncertainty.

This project aims to improve the current techniques used to assess the condition and safety of offshore and aerospace structures. The platforms used by the Oil and Gas industry in the North Sea were designed to operate for around 25 years in total. Over 600 of these platforms have now reached the end of their design life and the decision must be taken as to whether they can continue to be used safely or whether they should be decommissioned. For new offshore wind turbines, it is critical to have a good understanding of current structural condition so that maintenance can be planned optimally - unscheduled maintenance and downtime is extremely costly, owing to the difficulty of accessing these structures. Equally, in the aerospace industry, the ability to follow a condition-based maintenance strategy will save much time and money in avoiding unscheduled/emergency repair work. This project brings together researchers from the University of Sheffield, who are experts in Structural Health Monitoring and nonlinear system modelling, with industry experts who are leading the way in the monitoring and assessment of offshore and aerospace structures. The aim of this collaboration is to develop the most accurate means possible of assessing structural condition using monitoring data. The approach that will be taken here will combine the latest developments in artificial intelligence with more traditional methods that exploit understanding of the physics at work. Predictive models based on well-understood physics can often fall short of being able to explain complex behaviour, such as the loading an offshore structure will experience in a changing sea-state. This is where learning from measured data can be used to augment the model and improve prediction at times when the physics doesn't explain the behaviour captured by the sensors. The combination of physics and data-based models will be used to improve the prediction of the forcing a structure experiences from a changing environment. An accurate quantification of this enables one to calculate the stresses a structure has undergone, which leads to a prediction of its current condition. A similar modelling approach will be used to help make predictions about the structure itself. Finally, as well as improving the accuracy of the methods used to assess structural condition, the project aims to quantify the amount of uncertainty inherent in the models and algorithms that will be implemented. This approach acknowledges the fact that it is not always possible to make an accurate prediction of structural condition at a given time, but allows a confidence level to be assigned to each assessment made. To make responsible and optimal decisions concerning the repair or decommission of a structure, understanding the level of confidence one has in an assessment of structural condition is absolutely key.

The CDT will train the next generation of leaders in statistics and statistical machine learning, who will be able to develop widely-applicable novel methodology and theory, as well as create application-specific methods, leading to breakthroughs in real-world problems in government, medicine, industry and science. The research will focus on the development of applicable modern statistical theory and methods as well as on the underpinnings of statistical machine learning. The research will be strongly linked to applications. There is an urgent national need for graduates from this CDT. Large volumes of complicated data are now routinely collected in all sectors of society, encompassing electronic health records, massive scientific datasets, governmental data, and data collected through the advent of the digital economy. The underpinning techniques for exploiting these data come from statistics and machine learning. Exploiting such data is crucial for future UK prosperity. However, several reports from government and learned societies have identified a lack of individuals able to exploit this data. In many situations, existing methodology is insufficient. Off-the-shelf approaches may be misleading due to a lack of reproducibility or sampling biases which they do not correct. Furthermore, understanding the underlying mechanisms is often desired: scientifically valid, interpretable and reproducible results are needed to understand scientific phenomena and to justify decisions, particularly those affecting individuals. Bespoke, model-based statistical methods are needed, that may need to be blended with statistical machine learning approaches to deal with large data. Individuals that can fulfill these more sophisticated demands are doctoral level graduates in statistics who are well versed in the foundations of machine learning. Yet the UK only graduates a small number of statistics PhDs per year, and many of these graduates will not have been exposed to machine learning. The Centre will bring together Imperial and Oxford, two top statistics groups, as equal partners, offering an exceptional training environment and the direct involvement of absolute research leaders in their fields. The supervisor pool will include outstanding researchers in statistical methodology and theory as well as in statistical machine learning. We will use innovative and student-led teaching, focussing on PhD-level training. Teaching cuts across years and thus creates strong cohort cohesion not just within a year group but also between year groups. We will link theoretical advances to application areas through partner interactions as well as through a placement of students with users of statistics. The CDT has a large number of high profile partners that helped shape our application priority areas (digital economy, medicine, engineering, public health, science) and that will co-fund and co-supervise PhD students, as well as co-deliver teaching elements.