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  • Research impact and external collaboration

The Department of Mathematics and Statistics at the University of Reading has a long and established track record of impactful research, working with a wide variety of industrial and academic partners to achieve significant social and economic benefits.

We welcome partnership enquiries from commercial and industrial organisations – contact us for an initial project discussion. Our applied research covers a very wide range – from endangered elephants to better weather forecasts – as can be seen from some of our recent case studies below.

Cost-effective clinical trials development

Research into the design and analysis of clinical trials conducted within the Mathematics and Statistics Department of the University of Reading has the potential to cut development and regulatory costs, reduce time-to-market for new treatments, and improve patient outcomes.

Tackling the trade in illegal elephant ivory

The Mathematics and Statistics department at the University of Reading has, over many years, developed and improved statistical tools to monitor the world trade in illicit ivory. This work now underpins an international convention on endangered elephants.

Social media - measuring return on investment

Digital communications between individuals have resulted in large, multi-layered social networks that evolve from moment to moment. Although methods exist for analysing and modelling static networks, recent trends in communications, transport and energy have highlighted the need for methods that are appropriate for dynamic, evolving networks.

Improving ocean and climate forecasting

Atmospheric and oceanic forecasting systems require a vast quantity of input data collected from satellites, ocean buoys, aircraft and shipping, radiosondes, radar and ground stations. These data are incorporated into complex multi-scale models using data assimilation techniques. Improvements in such techniques developed at Reading enable better use of this expensively-acquired data to produce more accurate weather and climate predictions.

Analysing data for weather forecasting models

Effective and timely use of observational data is vital for forecasting any environmental system, and particularly so for weather forecasting because of the chaotic nature of the atmosphere. Research undertaken within the Mathematics and Statistics Department has led to better treatment of particular types of observational data in numerical weather prediction, resulting in significant improvements in operational analysis and forecast skill.

Simulation of fluid flow in oil or gas reservoirs

The more accurately fluid flow in a potential oil field can be assessed, the more efficiently and economically it can be developed, but the problem is an extremely complex one with many variables. Work undertaken at the University of Reading's Department of Mathematics and Statistics, working with Schlumberger PLC, developed a tool to provide fast, robust and efficient simulation of fluid flow in reservoirs.

Other external collaboration

In addition to the specific cases highlighted above, we also engage in a number of other exciting activities, applying our knowledge and expertise to a wide range of real world problems. Recent collaborative grants and consultancy agreements include:

Low carbon network solutions

Together with Scottish and Southern Energy, Honeywell, General Electrics, Bracknell Forest Council, EA Energy and DNV-GL, the University of Reading is a full partner in the Thames Valley Vision project demonstrating next generation low carbon network technology and solutions.

Our work includes the identification and analysis of energy usage patterns from smart meter data; forecasting and inference of short- and long-term demand on local networks that can be used by distribution network operators for planning and energy storage management; and modelling of future uptake of low carbon technologies.

Smart analytics

Counting Lab Ltd is a University of Reading spin out company providing smart analytics by developing data driven models and applications for customer/public facing sectors. Starting in December 2010, and exploiting know-how and prototypes developed within Mathematics and Statistics, Counting Lab is providing solutions through translational projects to many clients including Walmart, Net-a-porter, and the Centre for Defence Enterprise.

Large network analysis

Collaborative research with Unilever R&D on mathematical models of spread of mood through social networks and consultancy on modelling and analysis of large networks has been undertaken. Furthermore a research project with Counting Lab for Centre for Defence Enterprise considers the  dynamics of collective sentiment using large Twitter mentions network data-sets.

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