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.

Organisations

Publications

10 25 50

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