EPSRC Centre for Doctoral Training in Statistical Applied Mathematics at Bath (SAMBa)

Lead Research Organisation: University of Bath
Department Name: Mathematical Sciences

Abstract

We live in the "Era of Mathematics" (UKRI, 2018), in which mathematics research has deep and widespread impact. Medical imaging is enhanced using the theory of inverse problems. Predicting sewage contamination in waterways after storms requires solving complicated systems of hydrodynamic equations. Machine learning tools are revolutionising data-intensive computing and, handled with proper mathematical care, have vast potential benefits for science and society. These are examples of the ongoing explosion in mathematical innovation driving, and being driven by, the analysis and modelling of data running through every aspect of life.

Cutting-edge research now sits at the interface of data science and mathematical modelling. Methods and fields such as compressed sensing, stochastic optimisation, neural networks, Bayesian hierarchical models - to name but a few - have become interwoven and contributed to the delivery of a new domain of research. We refer to this research interface as "statistical applied mathematics".

Established in 2014, the Centre for Doctoral Training in Statistical Applied Mathematics at Bath (SAMBa, samba.ac.uk) delivers leading research and training in this space. In the development of this bid, we have consulted widely with academic, industrial, and governmental partners, who consistently report a large and widening gap between demand and supply for highly skilled graduates.

Our vision is to create a new generation of statistical applied mathematicians ready to lead high-impact, data-driven, mathematically-robust research in academia and industry. We will nurture a vibrant culture of cohort learning, enabling internationally-leading training in modern mathematical data science.

A particularly important research focus will be the synthesis of data-driven methods with robust mathematical modelling frameworks. Tomorrow's industrial mathematicians and statisticians must understand when machine learning tools are (and are not) appropriate to use and be able to conduct the underpinning research to improve these tools by integrating scientific domain knowledge.

This research challenge is informed by deep partnerships with a range of industry and government bodies. Our long-term partners such as BT, Syngenta, Novartis, the NHS, and the Environment Agency co-create our vision and our training. They are emphatic that we must address the urgent need for mathematical data science talent in this key strategic area for the UK economy. Many of our students will work directly on industry challenges during their PhD either in their core research or with internships. Our unique Integrative Think Tanks are the key mechanism for exploring new research ideas with industry. These are week-long events where SAMBa students, leading academics, and partners work together on industrial and societal problems.

SAMBa graduates will be able to develop and apply new ideas and methods to harness the power of data to tackle challenges affecting society, the economy, and the environment. Our students will move into academia, providing sustainability to the UK's capacity in this field, as well as industry and government, providing impact through societal benefits and driving economic growth. Many alumni now hold permanent positions at leading UK universities and senior positions in a range of businesses.

The CDT will be embedded within the University of Bath's Department of Mathematical Sciences, where 98% of the research is world leading or internationally excellent (REF2021). The CDT is supported by 58 academics in maths, with similar numbers of co-supervisors from industry and other departments. The centre will be co-delivered with 22 industry and government partners. A vital international perspective is provided by a worldwide network of 11 academic institutions sharing our scientific vision.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/Y034716/1 30/09/2024 30/03/2033
2928074 Studentship EP/Y034716/1 30/09/2024 29/09/2028 John Carlo DIMACULANGAN
2926396 Studentship EP/Y034716/1 30/09/2024 29/09/2028 Fernando PERAZZO
2929486 Studentship EP/Y034716/1 30/09/2024 29/09/2028 Wenzhi ZHONG
2930020 Studentship EP/Y034716/1 30/09/2024 29/09/2028 Clara HAWKINS
2926418 Studentship EP/Y034716/1 30/09/2024 30/03/2028 Robert JOHNSON
2928095 Studentship EP/Y034716/1 30/09/2024 29/09/2028 Bence KASZAS
2928098 Studentship EP/Y034716/1 30/09/2024 29/09/2028 Joshua PAYNE
2929485 Studentship EP/Y034716/1 30/09/2024 29/09/2028 Veronica RAFFETTO
2928111 Studentship EP/Y034716/1 30/09/2024 29/09/2028 Aengus ROBERTS
2928117 Studentship EP/Y034716/1 30/09/2024 29/09/2028 James TREISMAN
2928113 Studentship EP/Y034716/1 30/09/2024 29/09/2028 Harry SEMPLE
2928062 Studentship EP/Y034716/1 30/09/2024 29/09/2028 Peter CREW
2928087 Studentship EP/Y034716/1 30/09/2024 29/09/2028 Benjamin DUTTON