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Reference Number EP/Y034813/1
Title EPSRC Centre for Doctoral Training in Statistics and Machine Learning
Status Started
Energy Categories Other Cross-Cutting Technologies or Research 10%;
Not Energy Related 90%;
Research Types Training 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Statistics and Operational Research) 50%;
PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 50%;
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Dr SL Filippi

Imperial College London
Award Type Standard
Funding Source EPSRC
Start Date 01 April 2024
End Date 30 September 2032
Duration 102 months
Total Grant Value £7,873,682
Industrial Sectors Pharmaceuticals and Biotechnology; R&D; Retail; Transport Systems and Vehicles; Chemicals; Creative Industries; Energy; Financial Services; Information Technologies
Region London
Programme EPSRC Training Grants
Investigators Principal Investigator Dr SL Filippi , Mathematics, Imperial College London (99.992%)
  Other Investigator Dr EAK Cohen , Mathematics, Imperial College London (0.001%)
Professor M Cucuringu , Statistics, University of Oxford (0.001%)
Professor A Gandy , Mathematics, Imperial College London (0.001%)
Professor C Holmes , Statistics, University of Oxford (0.001%)
Dr C Pike Burke , Mathematics, Imperial College London (0.001%)
Professor P Rebeschini , Statistics, University of Oxford (0.001%)
Professor J Rousseau , Statistics, University of Oxford (0.001%)
Dr F Sanna Passino , Mathematics, Imperial College London (0.001%)
  Industrial Collaborator Project Contact , Monash University, Australia (0.000%)
Project Contact , Jaguar Land Rover Limited (0.000%)
Project Contact , Australian National University, Australia (0.000%)
Project Contact , IBM T.J. Watson Research Centre, USA (0.000%)
Project Contact , EURATOM/CCFE (0.000%)
Project Contact , Shell International Ltd (0.000%)
Project Contact , Los Alamos National Laboratory, USA (0.000%)
Project Contact , Novartis Pharma AG, Switzerland (0.000%)
Project Contact , Novo Nordisk A/S, Denmark (0.000%)
Project Contact , Office for National Statistics (0.000%)
Project Contact , Sandia National Laboratories, USA (0.000%)
Project Contact , ETH Zurich, Switzerland (0.000%)
Project Contact , McGill University, Canada (0.000%)
Project Contact , University of Western Australia (0.000%)
Project Contact , Stanford University, USA (0.000%)
Project Contact , University of Minnesota, USA (0.000%)
Project Contact , AWE Plc (0.000%)
Project Contact , Le Centre national de la recherche scientifique (CNRS), France (0.000%)
Project Contact , Aarhus University, Denmark (0.000%)
Project Contact , University College Dublin, Ireland (0.000%)
Project Contact , École polytechnique fédérale de Lausanne (EPFL), Switzerland (0.000%)
Project Contact , Columbia University, USA (0.000%)
Project Contact , University of Melbourne, Australia (0.000%)
Project Contact , British Broadcasting Corporation - BBC (0.000%)
Project Contact , King Abdullah University of Science and Technology, Saudi Arabia (0.000%)
Project Contact , Johns Hopkins University, USA (0.000%)
Project Contact , Microsoft Corporation (USA) (0.000%)
Project Contact , Pennsylvania State University, USA (0.000%)
Project Contact , Queensland University of Technology, Australia (0.000%)
Project Contact , University of Padua (Padova), Italy (0.000%)
Project Contact , University of Toronto, Canada (0.000%)
Project Contact , ASOS Plc (0.000%)
Project Contact , University of California Davis, USA (0.000%)
Project Contact , École Polytechnique, France (0.000%)
Project Contact , Facebook Inc., USA (0.000%)
Project Contact , Harvard University, USA (0.000%)
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Project Contact , GSK (0.000%)
Project Contact , Simon Fraser University (0.000%)
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Project Contact , BASF SE (0.000%)
Project Contact , University of Bologna (0.000%)
Project Contact , Addionics Limited (0.000%)
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Project Contact , Paris Dauphine University - PSL (0.000%)
Project Contact , 3C Capital Partners (0.000%)
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Project Contact , Deutsche Bank AG (UK) (0.000%)
Project Contact , Elemental Power Ltd (0.000%)
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Project Contact , University of Chicago (0.000%)
Web Site
Abstract The EPSRC Centre for Doctoral Training in Statistics and Machine Learning (StatML) will address the EPSRC research priority of the 'physical and mathematical sciences powerhouse' through an innovative cohort-based training program. StatML harnesses the combined strengths of Imperial and Oxford, two world-leading institutions in statistics and machine learning, in collaboration with a broad spectrum of industry partners, to nurture the next generation of leaders in this field. Our students will be at the forefront of advancing the core methodologies of data science and AI, crucial for unlocking the value inherent in data to benefit industry and society. They will be equipped with advanced research, technical, and practical skills, enabling them to make tangible real-world impacts. Our students will be ethical and responsible innovators, championing reproducible research and open science. Collaborating with students, charities and equality experts, StatML will also pioneer a comprehensive strategy to promote inclusivity, attract individuals from diverse backgrounds and eliminate biases. This will help diversify the UK's future statistics and machine learning workforce, essential for ensuring data science is used for public good.Data science and AI are now part of our everyday lives, transforming all sectors of the economy. To future-proof the UK's prosperity and security, it is essential to develop new methodology, specifically tailored to meet the big societal challenges of the future. The techniques underpinning such methods are founded in statistics and machine learning. Through close collaboration with a broad range of industry partners, our cohort-based training will support the UK in producing a critical mass of world-leading researchers with expertise in developing cutting-edge, impactful statistical and machine learning methodology and theory. It is well documented in government and learned society reports that the UK economy has an urgent need for these people. The significant level of industry support for our proposal also highlights the necessity of filling this gap in the UK data science ecosystem.StatML will learn from and build upon our previous successful experiences in cohort training of doctoral students (our existing StatML CDT funded in 2018, as well as other CDTs at Imperial and Oxford). Our students will continue to produce impactful, internationally leading research in statistics and machine learning (as evidenced by our students' impressive publication record and our world-leading research environment, as rated by the REF 2021 evaluation), while complementing this with a bespoke cohort-based Advanced Training program in Statistics and Machine Learning (StatML-AT). StatML-AT has been developed from our experience and in partnership with industry. It will be responsive to emerging technologies and equip our students with the practical skills required to transform how data is used. It will be delivered by our outstanding academics from both institutions alongside with industry leaders to ensure that students receive training in cutting edge technologies, along with the latest ideas in ethics, responsible innovation, sustainability and entrepreneurship. This will be complemented by industrial and academic placements to allow the students to develop their own international network and produce high-impact research. Together, StatML and its partners will train 90+ students over 5 cohorts. More than half of these will be funded from external sources, including 25+ by industry, representing excellent value for money. Our diverse cohorts will benefit from a unique and responsive training program combining academic excellence, industry engagement, and interdisciplinary culture. This will make StatML a vibrant research environment inspiring the next methodological advancements to transform the use of data and AI across industry and society

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Added to Database 03/07/24