EPSRC Centre for Doctoral Training in Statistical Applied Mathematics at Bath

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


SAMBa aims to create a generation of interdisciplinary mathematicians at the interface of stochastics, numerical analysis, applied mathematics, data science and statistics, preparing them to work as research leaders in academia and in industry in the expanding world of big models and big data. This research spectrum includes rapidly developing areas of mathematical sciences such as machine learning, uncertainty quantification, compressed sensing, Bayesian networks and stochastic modelling. The research and training engagement also encompasses modern industrially facing mathematics, with a key strength of our CDT being meaningful and long term relationships with industrial, government and other non-academic partners. A substantial proportion of our doctoral research will continue to be developed collaboratively through these partnerships.

The urgency and awareness of the UK's need for deep quantitative analytical talent with expert modelling skills has intensified since SAMBa's inception in 2014. Industry, government bodies and non-academic organisations at the forefront of technological innovation all want to achieve competitive advantage through the analysis of data of all levels of complexity. This need is as much of an issue outside of academia as it is for research and training capacity within academia and is reflected in our doctoral training approach.

The sense of urgency is evidenced in recent government policy (cf. Government Office for Science report "Computational Modelling, Technological Futures, 2018"), through the EPSRC CDT priority areas of Mathematical and Computational Modelling and Statistics for the 21st century as well as through our own experience of growing SAMBa since 2014. We have had extensive collaboration with partners from a wide range of UK industrial sectors (e.g. agri-science, healthcare, advanced materials) and government bodies (e.g. NHS, National Physical Laboratory, Environment Agency and Office for National Statistics) and our portfolio is set to expand.

SAMBa's approach to doctoral training, developed in conjunction with our industrial partners, will create future leaders both in academia and industry and consists of:

- A broad-based first year developing mathematical expertise across the full range of Statistical Applied Mathematics, tailored to each incoming student.

- Deep experience in academic-industrial collaboration through Integrative Think Tanks: bespoke problem-formulation workshops developed by SAMBa.

- Research training in a department which produces world-leading research in Statistical Applied Mathematics.

- Multiple cohort-enhanced training activities that maximise each student's talents and includes mentoring through cross-cohort integration.

- Substantial international opportunities such as academic placements, overseas workshops and participation in jointly delivered ITTs.

- The opportunity for co-supervision of research from industrial and non-maths academic supervisors, including student placements in industry.

This proposal will initially fund over 60 scholarships, with the aim to further increase this number through additional funding from industrial and international partners. Based on the CDT's current track record from its inception in 2014 (creating 25 scholarships to add to an initial investment of 50), our target is to deliver 90 PhD students over the next five years. With 12 new staff positions committed to SAMBa-core areas since 2015, students in the CDT cohort will benefit from almost 60 Bath Mathematical Sciences academics available for lead supervisory roles, as well as over 50 relevant co-supervisors in other departments.

Planned Impact

Combining specialised modelling techniques with complex data analysis in order to deliver prediction with quantified uncertainties lies at the heart of many of the major challenges facing UK industry and society over the next decades. Indeed, the recent Government Office for Science report "Computational Modelling, Technological Futures, 2018" specifies putting the UK at the forefront of the data revolution as one of their Grand Challenges.

The beneficiaries of our research portfolio will include a wide range of UK industrial sectors such as the pharmaceutical industry, risk consultancy, telecommunications and advanced materials, as well as government bodies, including the NHS, the Met Office and the Environment Agency.

Examples of current impactful projects pursued by students and in collaboration with stake-holders include:

- Using machine learning techniques to develop automated assessment of psoriatic arthritis from hand X-Rays, freeing up consultants' time (with the NHS).

- Uncertainty quantification for the Neutron Transport Equation improving nuclear reactor safety (co-funded by Wood).

- Optimising the resilience and self-configuration of communication networks with the help of random graph colouring problems (co-funded by BT).

- Risk quantification of failure cascades on oil platforms by using Bayesian networks to improve safety assessment for certification (co-funded by DNV-GL).

- Krylov regularisation in a Bayesian framework for low-resolution Nuclear Magnetic Resonance to assess properties of porous media for real-time exploration (co-funded by Schlumberger).

- Machine learning methods to untangle oceanographic sound data for a variety of goals in including the protection of wildlife in shipping lanes (with the Department of Physics).

Future committed partners for SAMBa 2.0 are: BT, Syngenta, Schlumberger, DNV GL, Wood, ONS, AstraZeneca, Roche, Diamond Light Source, GKN, NHS, NPL, Environment Agency, Novartis, Cytel, Mango, Moogsoft, Willis Towers Watson.

SAMBa's core mission is to train the next generation of academic and industrial researchers with the breadth and depth of skills necessary to address these challenges. SAMBa's most sustained impact will be through the contributions these researchers make over the longer term of their careers. To set the students up with the skills needed to maximise this impact, SAMBa has developed a bespoke training experience in collaboration with industry, at the heart of its activities. Integrative Think Tanks (ITTs) are week-long workshops in which industrial partners present high-level research challenges to students and academics. All participants work collaboratively to formulate mathematical
models and questions that address the challenges. These outputs are meaningful both to the non-academic partner, and as a mechanism for identifying mathematical topics which are suitable for PhD research. Through the co-ownership of collaboratively developed projects, SAMBa has the capacity to lead industry in capitalising on recent advances in mathematics. ITTs occur twice a year and excel in the process of problem distillation and formulation, resulting in an exemplary environment for developing impactful projects.

SAMBa's impact on the student experience will be profound, with training in a broad range of mathematical areas, in team working, in academic-industrial collaborations, and in developing skills in communicating with specialist and generalist audiences about their research. Experience with current SAMBa students has proven that these skills are highly prized: "The SAMBa approach was a great template for setting up a productive, creative and collaborative atmosphere. The commitment of the students in getting involved with unfamiliar areas of research and applying their experience towards producing solutions was very impressive." - Dr Mike Marsh, Space weather researcher, Met Office.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S022945/1 01/10/2019 31/03/2028
2279484 Studentship EP/S022945/1 30/09/2019 30/09/2023 Joshua INOUE
2281605 Studentship EP/S022945/1 30/09/2019 30/09/2023 Piotr MORAWIECKI
2278905 Studentship EP/S022945/1 30/09/2019 30/09/2023 Christopher DEAN
2284235 Studentship EP/S022945/1 30/09/2019 30/09/2023 Jeremy WORSFOLD
2282147 Studentship EP/S022945/1 30/09/2019 30/09/2023 Shahzeb RAJA NOUREEN
2281145 Studentship EP/S022945/1 30/09/2019 30/09/2023 Rosa KOWALEWSKI
2281158 Studentship EP/S022945/1 30/09/2019 30/09/2023 Yi Sheng LIM
2282421 Studentship EP/S022945/1 30/09/2019 30/09/2023 Katie PHILLIPS
2284054 Studentship EP/S022945/1 30/09/2019 30/09/2023 Carlo SCALI
2284242 Studentship EP/S022945/1 30/09/2019 30/09/2023 Edwin WATSON-MILLER
2371934 Studentship EP/S022945/1 01/10/2019 30/09/2024 Fengpei WANG
2440945 Studentship EP/S022945/1 01/10/2020 30/09/2024 Marcel STOZIR
2436904 Studentship EP/S022945/1 01/10/2020 30/09/2024 Allen PAUL
2437094 Studentship EP/S022945/1 01/10/2020 30/09/2024 Matthew Pawley
2436441 Studentship EP/S022945/1 01/10/2020 30/09/2024 Mehar MOTALA
2445329 Studentship EP/S022945/1 01/10/2020 30/09/2024 Andrei SONTAG
2437224 Studentship EP/S022945/1 01/10/2020 30/09/2024 Jennifer POWER
2436352 Studentship EP/S022945/1 01/10/2020 30/09/2024 Sonny MEDINA JIMENEZ
2437107 Studentship EP/S022945/1 01/10/2020 30/09/2024 Timothy PETERS
2441582 Studentship EP/S022945/1 01/10/2020 30/09/2024 Carmen VAN-DE-L'ISLE
2441482 Studentship EP/S022945/1 01/10/2020 30/09/2024 Salvador ESQUIVEL CALZADA
2441793 Studentship EP/S022945/1 01/10/2020 30/09/2024 Fraser Robert WATERS