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University of Bristol’s new research to get big benefits for everyone

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The Network Stochastic Processes and Time Series (NeST) programme aims to develop new ways of extracting useful information from particular types of huge, complex datasets.

The aim is to achieve a step change in the modelling and analysis of vast banks. These banks are ever-growing, often interconnected data relating to customer needs and behaviour and the performance of systems and equipment.

Across many sectors, this will make it easier to pinpoint problems and opportunities, make accurate predictions and plan robustly.

Dovetailing leading-edge expertise in statistics, probability theory and data science, the six-year programme is being funded by the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).

NeST involves six universities:

University of Bath
University of Bristol
Imperial College London
University of Oxford
University of York
London School of Economics
It also involves a range of companies and government organisations. Partners include:

the Office for National Statistics
Financial Network Analytics
Government Communications Headquarters
The ambition is for NeST to establish itself as the world’s leading research centre in the development of new theory, methods and computational techniques. It will focus on tackling the mathematical and statistical analysis of datasets generated by ‘dynamic networks’.

These include not just IT networks, big and small, but also networks in the wider and more traditional sense. For example, the railway network and all the railway lines and connection points (such as stations, where the network connects with customers) that this incorporates.

The dynamic aspect of networks is particularly important: most datasets are not static but are constantly evolving and growing.

This maths research has multiple potential fields of application and is targeting, for example:

More secure, greener power grids: greater use of renewables is key to the UK’s energy security and its ability to achieve net zero carbon emissions. Integrating intermittent energy sources such as wind and solar requires sophisticated forecasting of net demand on power networks. NeST will develop computer models and simulations that help meet this challenge.
Better detection of cyberattacks: in 2022, cybercrime cost global businesses, consumers and governments an estimated £1 trillion. Innovative tools are urgently needed to make IT networks safer. NeST will develop new ways of analysing network traffic to pinpoint tell-tale changes indicative of cyberattacks, enabling earlier detection and reducing damage caused.
Stronger protection of human rights. Some of the greatest harms to society arise through organised forms of exploitation, including human trafficking and corruption. NeST will apply dynamic network methods to real-world data collected to tackle such challenges, enabling deeper insights into the structure and dynamics of exploitation, as well as the networks of relationships that allow such organised exploitation to take place.
Improved mail services: mail companies face many logistical challenges to enhance the efficiency of their services. NeST will help them match resources to changing demand and better utilise their distribution infrastructure and vehicle fleets. Benefits will include improved services for business and the public, plus significant cuts to carbon footprints.
Jane Nicholson, EPSRC Director for Research Base, said: “The NeST programme demonstrates the fundamental importance of the mathematical sciences to important sectors such as energy, transport and cybersecurity. The team’s work in establishing itself as a leader in the study and exploitation of dynamic networks, which will reflect the fact that the data which underpin these critical sectors is constantly changing, will deliver benefits for industry and key services which impact on our daily lives.”

Professor Patrick Rubin-Delanchy, one of the Deputy Directors of NeST from the School of Mathematics at the University of Bristol, added: “We can solve important scientific and societal problems if we can better understand dynamic networks. This grant will allow us to build a national centre bringing a highly diverse community of researchers together, with backgrounds in statistics, probability and data science, to advance our understanding and techniques.”