Area of investment and support

Area of investment and support: Statistics and applied probability

Statistical methodology and development of new probabilistic techniques inspired by applications.

Partners involved:
Engineering and Physical Sciences Research Council (EPSRC)

The scope and what we're doing

Statistical methodology and development of new probabilistic techniques inspired by applications, including research in stochastic and probabilistic modelling and inference in stochastic systems.

This is an area of strength for the UK and importance for many scientific disciplines. Despite substantial growth since 2015, demand is undiminished for qualified statisticians with an understanding of application areas including data analytics, healthcare modelling and artificial intelligence.

Statistics and applied probability research has major impacts in many areas, as evidenced by its contribution to EPSRC’s organisational priorities. It is a major contributor to advances in data science, healthcare and the digital economy.

We will develop a focus on statistics, in order to balance the research area, and we will encourage applications with a high proportion of fundamental statistics. This can be coupled with applied probability, fundamentals for AI, big data or model development, but the majority of the work must fall under the remit of statistics.

This strategy aims to build on recent investment in the area (such as the Alan Turing Institute), address capacity issues throughout the ‘people pipeline’ and respond to growing demand across all sectors of economy and society by growing investment and supporting people at all career stages within this research area.

Aims

Developing our portfolio

We will support research and training that builds on and complements previous and current work, including activities by the Alan Turing Institute.

This will mean maintaining support for core fundamental statistical methodologies, while developing links with more applied areas of statistics across the entire research landscape, both within EPSRC’s domains and more broadly.

Outputs from the Statistics and Applied Probability Landscape Event (STAPLE) will be used to develop a highly collaborative portfolio.

Supporting the GCRF

We will support research aligned to the Global Challenges Research Fund (GCRF), for example in the areas of uncertainty and epidemiology.

Skills support

We will respond to the growing demand for people with skills in statistics and applied probability, particularly at the early-career stage. It is important to ensure that people have skills across all areas of statistics and applied probability, as well as spanning key topics such as machine learning, data analytics, uncertainty quantification and medical statistics.

Why we're doing it

The UK has an international reputation for expertise in a number of statistical methods, including medical statistics, Bayesian statistics, interface with genomics, machine learning and big data.

There are strong connections between statistics and applied probability, and an array of applications in sciences, industry, business and government. These provide economic, industrial and societal impact in a range of applications and sectors, including healthcare and modelling financial and environmental uncertainty.

Statistics and applied probability is therefore an important research area that connects to and supports a number of other research areas, key topics and disciplines, such as healthcare, data analytics and uncertainty quantification. This importance is reflected in the 2012 Deloitte report, the 2014 and 2016 EPSRC statistics and applied probability theme days, the 2014 Statistics Strategy from the Office for National Statistics, and the area’s relevance to large investments, including the Alan Turing Institute.

Over half of EPSRC investments in this area are relevant to industrial sectors such as healthcare, environment, financial services and energy. The research area is also relevant to the eight great technologies – primarily big data, and robotics and autonomous systems.

Statistics and applied probability has links to many research areas and themes across EPSRC – most notably within Information and Communication Technologies (ICT), Maths, Healthcare Technologies, and Digital Economies.

Within the mathematical sciences, the primary connections are with Numerical Analysis, and Mathematical Analysis. Across EPSRC, the primary connections are with the Artificial Intelligence Technologies, Healthcare Technologies and Manufacturing the Future themes.

Statistics and applied probability researchers have a broad range of skills, including modelling, optimisation techniques, uncertainty quantification, data analytics and machine learning. There is demand from industry to recruit researchers with this knowledge of fundamental statistical and probabilistic methodology, but a recognised shortage of these skills in the UK is a concern.

Since 2015, capacity has grown at all career stages, and recruitment at PhD level is healthy, with five centres for doctoral training (CDTs) supported in this area. But there is still significant concern about recruitment and retention of skilled academics and the threat of key capacity being lost from UK academia to industry, with universities being unable to compete with industry. In this regard, academia has the opportunity to complement industry’s research interests rather than trying to replicate them, and there is a need to support career progression at all stages – especially the early academic career stage.

View evidence sources used to inform our research strategies.

Past projects, outcomes and impact

Visualising our portfolio (VoP) is a tool for users to visually interact with the EPSRC portfolio and data relationships. Find out more about research area connections and funding for Statistics and Applied Probability.

Find previously funded projects on Grants on the Web.

Who to contact

Jacob Wood, Portfolio Manager

Email: jacob.wood@epsrc.ukri.org

Telephone: 07925 348098

Alternatively, email: maths@epsrc.ukri.org

Last updated: 22 April 2022

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