EPSRC Centre for Doctoral Training in Statistics and Machine Learning
Lead Research Organisation:
Imperial College London
Department Name: Mathematics
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.
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.
Organisations
- Imperial College London (Lead Research Organisation)
- OFFICE FOR NATIONAL STATISTICS (Project Partner)
- Simon Fraser University (Project Partner)
- ETH Zurich (Project Partner)
- Novo Nordisk (Denmark) (Project Partner)
- University of Padua (Project Partner)
- Centre National de la Recherche Scient. (Project Partner)
- University of California, Davis (Project Partner)
- University of Minnesota (Project Partner)
- Alpine Intuition Sarl (Project Partner)
- Johns Hopkins University (Project Partner)
- Optima Partners (Project Partner)
- Harvard University (Project Partner)
- AIMS (Project Partner)
- Spectra Analytics (Project Partner)
- J.P. Morgan (Project Partner)
- Sandia National Laboratories California (Project Partner)
- Aarhus University (Project Partner)
- King Abdullah University of Science and Technology (Project Partner)
- MediaTek (Project Partner)
- Addionics Limited (Project Partner)
- Bocconi University (Project Partner)
- Martingale Foundation (Project Partner)
- ELEMENTAL POWER LTD (Project Partner)
- École Polytechnique Fédérale de Lausanne (Project Partner)
- McGill University (Project Partner)
- University of Melbourne (Project Partner)
- Los Alamos National Laboratory (Project Partner)
- Kaiju Capital Management Limited (Project Partner)
- CCFE/UKAEA (Project Partner)
- Qube Research & Technologies (Project Partner)
- IBM Research (Project Partner)
- British Broadcasting Corporation (United Kingdom) (Project Partner)
- Novartis (United States) (Project Partner)
- Spotify UK (Project Partner)
- Microsoft (United States) (Project Partner)
- Shell (United Kingdom) (Project Partner)
- University of Toronto (Project Partner)
- Monash University (Project Partner)
- Rakai Health Sciences Program (Project Partner)
- Columbia University (Project Partner)
- ASOS Plc (Project Partner)
- Instituto de Medicina Tropical (Project Partner)
- University College Dublin (Project Partner)
- GlaxoSmithKline (United Kingdom) (Project Partner)
- Guido Carli Free International University for Social Studies (Project Partner)
- Atomic Weapons Establishment (Project Partner)
- 3C Capital Partners (Project Partner)
- University of Bologna (Project Partner)
- Tata Motors (United Kingdom) (Project Partner)
- Criteo Technology (Project Partner)
- Korea Advanced Institute of Science and Technology (Project Partner)
- École Polytechnique (Project Partner)
- Stanford University (Project Partner)
- American Express (Project Partner)
- PANGEA-HIV consortium (Project Partner)
- University of Paris (Project Partner)
- Deutsche Bank (United Kingdom) (Project Partner)
- Leibniz Institute for Prevention Researc (Project Partner)
- Cancer Research UK Convergence Science (Project Partner)
- Queensland University of Technology (Project Partner)
- G-Research (Project Partner)
- University of Chicago (Project Partner)
- BASF (Germany) (Project Partner)
- University of Western Australia (Project Partner)
- Australian National University (Project Partner)
- CausaLens (Project Partner)
- The University of Texas MD Anderson Cancer Center (Project Partner)
- Meta (Project Partner)
- NewDay Cards Ltd (Project Partner)
- Duke University (Project Partner)
- VU Amsterdam (Project Partner)
- Arctic Wolf Networks (Project Partner)
- Securonix (Project Partner)
- dunnhumby Limited (Project Partner)
- Pennsylvania State University (Project Partner)
- In2science UK (Project Partner)
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/Y034813/1 | 31/03/2024 | 29/09/2032 | |||
2927517 | Studentship | EP/Y034813/1 | 30/09/2024 | 29/09/2028 | Jamie Reason |
2928225 | Studentship | EP/Y034813/1 | 30/09/2024 | 29/09/2028 | Joanna Marks |
2928221 | Studentship | EP/Y034813/1 | 30/09/2024 | 29/09/2028 | Michael Scoones |
2928418 | Studentship | EP/Y034813/1 | 30/09/2024 | 29/09/2028 | Lucas Siu |
2928176 | Studentship | EP/Y034813/1 | 30/09/2024 | 29/09/2028 | Adhithya Saravanan |
2928131 | Studentship | EP/Y034813/1 | 30/09/2024 | 29/09/2028 | Alexander Yan |
2928388 | Studentship | EP/Y034813/1 | 30/09/2024 | 29/09/2028 | Rafael Athanasiades |
2928591 | Studentship | EP/Y034813/1 | 30/09/2024 | 29/09/2028 | Ruizi Yan |
2928212 | Studentship | EP/Y034813/1 | 30/09/2024 | 29/09/2028 | Peter Hyland |
2928603 | Studentship | EP/Y034813/1 | 30/09/2024 | 29/09/2028 | Deepro Choudhury |
2928402 | Studentship | EP/Y034813/1 | 30/09/2024 | 29/09/2028 | James Cuin |
2928198 | Studentship | EP/Y034813/1 | 30/09/2024 | 29/09/2028 | Isaac Hayden |
2928780 | Studentship | EP/Y034813/1 | 30/09/2024 | 29/09/2028 | Zhuoyue Huang |
2928364 | Studentship | EP/Y034813/1 | 30/09/2024 | 29/09/2028 | Inga Armann |
2928197 | Studentship | EP/Y034813/1 | 30/09/2024 | 29/09/2028 | Bogdana Jelic |
2931845 | Studentship | EP/Y034813/1 | 28/10/2024 | 27/10/2028 | Daniil Shmelev |