EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning

Lead Research Organisation: Imperial College London
Department Name: Mathematics


The CDT will train the next generation of leaders in statistics and statistical machine learning, who will be able to develop widely-applicable novel methodology and theory, as well as create application-specific methods, leading to breakthroughs in real-world problems in government, medicine, industry and science. The research will focus on the development of applicable modern statistical theory and methods as well as on the underpinnings of statistical machine learning. The research will be strongly linked to applications.
There is an urgent national need for graduates from this CDT. Large volumes of complicated data are now routinely collected in all sectors of society, encompassing electronic health records, massive scientific datasets, governmental data, and data collected through the advent of the digital economy. The underpinning techniques for exploiting these data come from statistics and machine learning. Exploiting such data is crucial for future UK prosperity. However, several reports from government and learned societies have identified a lack of individuals able to exploit this data.
In many situations, existing methodology is insufficient. Off-the-shelf approaches may be misleading due to a lack of reproducibility or sampling biases which they do not correct. Furthermore, understanding the underlying mechanisms is often desired: scientifically valid, interpretable and reproducible results are needed to understand scientific phenomena and to justify decisions, particularly those affecting individuals. Bespoke, model-based statistical methods are needed, that may need to be blended with statistical machine learning approaches to deal with large data. Individuals that can fulfill these more sophisticated demands are doctoral level graduates in statistics who are well versed in the foundations of machine learning. Yet the UK only graduates a small number of statistics PhDs per year, and many of these graduates will not have been exposed to machine learning.
The Centre will bring together Imperial and Oxford, two top statistics groups, as equal partners, offering an exceptional training environment and the direct involvement of absolute research leaders in their fields. The supervisor pool will include outstanding researchers in statistical methodology and theory as well as in statistical machine learning.
We will use innovative and student-led teaching, focussing on PhD-level training. Teaching cuts across years and thus creates strong cohort cohesion not just within a year group but also between year groups. We will link theoretical advances to application areas through partner interactions as well as through a placement of students with users of statistics.
The CDT has a large number of high profile partners that helped shape our application priority areas (digital economy, medicine, engineering, public health, science) and that will co-fund and co-supervise PhD students, as well as co-deliver teaching elements.

Planned Impact

The primary CDT impact will be training 75 PhD graduates as the next generation of leaders in statistics and statistical machine learning. These graduates will lead in industry, government, health care, and academic research. They will bridge the gap between academia and industry, resulting in significant knowledge transfer to both established and start-up companies. Because this cohort will also learn to mentor other researchers, the CDT will ultimately address a UK-wide skills gap. The students will also be crucial in keeping the UK at the forefront of methodological research in statistics and machine learning.
After graduating, students will act as multipliers, educating others in advanced methodology throughout their career. There are a range of further impacts:
- The CDT has a large number of high calibre external partners in government, health care, industry and science. These partnerships will catalyse immediate knowledge transfer, bringing cutting edge methodology to a large number of areas. Knowledge transfer will also be achieved through internships/placements of our students with users of statistics and machine learning.
- Our Women in Mathematics and Statistics summer programme is aimed at students who could go on to apply for a PhD. This programme will inspire the next generation of statisticians and also provide excellent leadership training for the CDT students.
- The students will develop new methodology and theory in the domains of statistics and statistical machine learning. It will be relevant research, addressing the key questions behind real world problems. The research will be published in the best possible statistics journals and machine learning conferences and will be made available online. To maximize reproducibility and replicability, source code and replication files will be made available as open source software or, when relevant to an industrial collaboration, held as a patent or software copyright.



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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023151/1 31/03/2019 29/09/2027
2260831 Studentship EP/S023151/1 30/09/2019 29/09/2023 Daniel Moss
2247853 Studentship EP/S023151/1 30/09/2019 29/09/2023 Stefanos Bennett
2283505 Studentship EP/S023151/1 30/09/2019 29/09/2023 Harrison Zhu
2605899 Studentship EP/S023151/1 30/09/2019 30/03/2024 Adam Howes
2635640 Studentship EP/S023151/1 30/09/2019 29/09/2023 Jason Clarkson
2247701 Studentship EP/S023151/1 30/09/2019 29/09/2023 Michael Hutchinson
2283474 Studentship EP/S023151/1 30/09/2019 06/12/2023 Antoine Meyer
2247906 Studentship EP/S023151/1 30/09/2019 29/09/2023 Sahra Ghalebikesabi
2247868 Studentship EP/S023151/1 30/09/2019 29/09/2023 Jason Clarkson
2635637 Studentship EP/S023151/1 30/09/2019 29/09/2023 Stefanos Bennett
2283002 Studentship EP/S023151/1 30/09/2019 30/03/2024 James Wei
2641932 Studentship EP/S023151/1 30/09/2019 31/12/2022 Melodie Monod
2247869 Studentship EP/S023151/1 30/09/2019 29/09/2023 Anna Menacher
2282781 Studentship EP/S023151/1 30/09/2019 29/09/2023 Phillip Murray
2284224 Studentship EP/S023151/1 30/09/2019 29/09/2023 Chak Hin Bryan Liu
2282778 Studentship EP/S023151/1 30/09/2019 31/03/2024 Enrico Crovini
2248365 Studentship EP/S023151/1 30/09/2020 29/09/2024 James Topping
2635642 Studentship EP/S023151/1 30/09/2020 29/09/2024 Lucile Ter-Minassian
2635641 Studentship EP/S023151/1 30/09/2020 29/09/2024 Oscar Clivio
2420816 Studentship EP/S023151/1 30/09/2020 29/09/2024 Lucile Ter-Minassian
2420792 Studentship EP/S023151/1 30/09/2020 29/09/2024 Desislava Ivanova
2420772 Studentship EP/S023151/1 30/09/2020 29/09/2024 Andrew Campbell
2635643 Studentship EP/S023151/1 30/09/2020 29/09/2024 Zoi Tsangalidou
2420820 Studentship EP/S023151/1 30/09/2020 29/09/2024 Zoi Tsangalidou
2420649 Studentship EP/S023151/1 30/09/2020 29/09/2024 Oscar Clivio
2632835 Studentship EP/S023151/1 02/10/2020 29/09/2024 Michael Komodromos
2446052 Studentship EP/S023151/1 02/10/2020 29/09/2024 Andrea Brizzi
2445745 Studentship EP/S023151/1 02/10/2020 29/09/2024 Benjamin Howson
2605895 Studentship EP/S023151/1 02/10/2020 29/09/2024 Jose Folch Urroz
2605897 Studentship EP/S023151/1 02/10/2020 29/09/2024 Xing Liu
2605900 Studentship EP/S023151/1 02/10/2020 29/09/2024 Ben Tu
2605902 Studentship EP/S023151/1 02/10/2020 29/09/2024 Michael Komodromos
2605889 Studentship EP/S023151/1 02/10/2020 29/09/2024 Alexander larionov
2446166 Studentship EP/S023151/1 02/10/2020 29/09/2024 Thomas Matcham
2442432 Studentship EP/S023151/1 02/10/2020 29/09/2024 Tresnia Berah
2564817 Studentship EP/S023151/1 30/09/2021 29/09/2025 Angus Phillips
2564803 Studentship EP/S023151/1 30/09/2021 29/09/2025 Max Anderson Loake
2602755 Studentship EP/S023151/1 30/09/2021 29/09/2025 Yu Chen
2565026 Studentship EP/S023151/1 30/09/2021 29/09/2025 Nicholas Steyn
2565020 Studentship EP/S023151/1 30/09/2021 29/09/2025 Vikrant Shirvaikar
2564794 Studentship EP/S023151/1 30/09/2021 29/09/2025 Joseph Benton
2564812 Studentship EP/S023151/1 30/09/2021 29/09/2025 Alex Buna Marginean
2602524 Studentship EP/S023151/1 01/10/2021 29/08/2025 Emmeran Johnson
2602530 Studentship EP/S023151/1 01/10/2021 29/08/2025 Shahriar kazi
2602507 Studentship EP/S023151/1 01/10/2021 29/08/2025 Efthymios COSTA
2602756 Studentship EP/S023151/1 01/10/2021 29/08/2025 Quiquan Wang
2602754 Studentship EP/S023151/1 01/10/2021 29/08/2025 Fabio Feser
2602749 Studentship EP/S023151/1 01/10/2021 29/08/2025 Yijin Zeng
2740734 Studentship EP/S023151/1 30/09/2022 29/09/2026 Nicolas Petit
2740612 Studentship EP/S023151/1 30/09/2022 29/09/2026 Deepak Badarinath
2740715 Studentship EP/S023151/1 30/09/2022 29/09/2026 Samuel Howard
2740634 Studentship EP/S023151/1 30/09/2022 29/09/2026 Stefano Cortinovis
2740743 Studentship EP/S023151/1 30/09/2022 29/09/2026 Jeffrey Tse
2740724 Studentship EP/S023151/1 30/09/2022 29/09/2026 George Hutchings
2740759 Studentship EP/S023151/1 30/09/2022 29/09/2026 Linying Yang
2740739 Studentship EP/S023151/1 30/09/2022 29/09/2026 Anya Sims
2740638 Studentship EP/S023151/1 30/09/2022 29/09/2026 Alexander Forster
2748969 Studentship EP/S023151/1 02/10/2022 29/09/2026 Hetvi Jethwani
2748915 Studentship EP/S023151/1 02/10/2022 29/09/2026 Guiomar Pescador Barrios
2748829 Studentship EP/S023151/1 02/10/2022 29/09/2026 Marcos Tapia Costa
2748724 Studentship EP/S023151/1 02/10/2022 29/09/2026 Joshua Corneck-Willcox
2749396 Studentship EP/S023151/1 02/10/2022 29/09/2026 Pavithra Srinath
2748527 Studentship EP/S023151/1 02/10/2022 29/09/2026 Brendan Martin
2891754 Studentship EP/S023151/1 30/09/2023 29/09/2027 DA Shozen
2886777 Studentship EP/S023151/1 30/09/2023 29/09/2027 Marcel Hedman
2886365 Studentship EP/S023151/1 30/09/2023 29/09/2027 Kianoosh Ashouritaklimi
2886732 Studentship EP/S023151/1 30/09/2023 29/09/2027 Peter Potaptchik
2891773 Studentship EP/S023151/1 30/09/2023 29/09/2027 Toby Boyne
2891756 Studentship EP/S023151/1 30/09/2023 29/09/2027 PAULA Cordero Encina
2891686 Studentship EP/S023151/1 30/09/2023 29/09/2027 Anya Iskakova
2891802 Studentship EP/S023151/1 30/09/2023 29/09/2027 Shavindra Jayasekera
2886709 Studentship EP/S023151/1 30/09/2023 29/09/2027 Leo Zhang
2891705 Studentship EP/S023151/1 30/09/2023 29/09/2027 Paul Valsecchi Oliva
2886852 Studentship EP/S023151/1 30/09/2023 29/09/2027 Qinyu Li
2886858 Studentship EP/S023151/1 30/09/2023 29/09/2027 Rafaël Brutti
2891663 Studentship EP/S023151/1 30/09/2023 29/09/2027 Vanessa Madu
2891741 Studentship EP/S023151/1 30/09/2023 29/09/2027 Keyi Jiang
2891767 Studentship EP/S023151/1 30/09/2023 29/09/2027 REBECCA Langdon
2886723 Studentship EP/S023151/1 30/09/2023 29/09/2027 Jack Foxabbott