EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning
Lead Research Organisation:
Imperial College London
Department Name: Mathematics
Abstract
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.
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.
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.
Organisations
- Imperial College London (Lead Research Organisation)
- Prowler.io (Project Partner)
- Harvard University (Project Partner)
- Schlumberger (United Kingdom) (Project Partner)
- J.P. Morgan (Project Partner)
- Qualcomm (United States) (Project Partner)
- QuantumBlack (Project Partner)
- Babylon Health (Project Partner)
- Rosalind Franklin Institute (Project Partner)
- Winnow Solutions Limited (Project Partner)
- Leiden University (Project Partner)
- DeepMind (United Kingdom) (Project Partner)
- University College London (Project Partner)
- Microsoft Research (United Kingdom) (Project Partner)
- Bocconi University (Project Partner)
- AIMS Rwanda (Project Partner)
- École Polytechnique Fédérale de Lausanne (Project Partner)
- University of British Columbia (Project Partner)
- Los Alamos National Laboratory (Project Partner)
- Ludwig-Maximilians-Universität München (Project Partner)
- Research Organization of Information and Systems (Project Partner)
- Microsoft (United States) (Project Partner)
- Joint United Nations Programme on HIV/AIDS (Project Partner)
- Tesco (United Kingdom) (Project Partner)
- Select Statistical Services (Project Partner)
- Cogent Labs (Project Partner)
- Columbia University (Project Partner)
- Amazon (Germany) (Project Partner)
- ASOS Plc (Project Partner)
- BP (United Kingdom) (Project Partner)
- Albora Technologies (Project Partner)
- Samsung (United Kingdom) (Project Partner)
- Element AI (Project Partner)
- Cortexica (United Kingdom) (Project Partner)
- Novartis (Switzerland) (Project Partner)
- The Alan Turing Institute (Project Partner)
- Carnegie Mellon University (Project Partner)
- RIKEN (Project Partner)
- Bill & Melinda Gates Foundation (Project Partner)
- Facebook UK (Project Partner)
- Centers for Disease Control and Prevention (Project Partner)
- University of Paris (Project Partner)
- University of Washington (Project Partner)
- Office for National Statistics (Project Partner)
- Queensland University of Technology (Project Partner)
- Centrica (United Kingdom) (Project Partner)
- BASF (Germany) (Project Partner)
- Mercedes-Benz Grand prix Ltd (Project Partner)
- Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (Project Partner)
- University of California, Berkeley (Project Partner)
- African Institute for Mathematical Sciences (Project Partner)
- Filtered Technologies (Project Partner)
- Cervest Limited (Project Partner)
- The Francis Crick Institute (Project Partner)
- Heidelberg Institute for Theoretical Studies (Project Partner)
- Manufacturing Technology Centre (United Kingdom) (Project Partner)
- United Kingdom Atomic Energy Authority (Project Partner)
- Tencent (China) (Project Partner)
- Vector Institute (Project Partner)
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/S023151/1 | 01/04/2019 | 30/09/2027 | |||
| 2260831 | Studentship | EP/S023151/1 | 01/10/2019 | 30/09/2023 | Daniel Moss |
| 2247853 | Studentship | EP/S023151/1 | 01/10/2019 | 30/09/2023 | Stefanos Bennett |
| 2283505 | Studentship | EP/S023151/1 | 01/10/2019 | 30/09/2023 | Harrison Zhu |
| 2605899 | Studentship | EP/S023151/1 | 01/10/2019 | 30/03/2024 | Adam Howes |
| 2635640 | Studentship | EP/S023151/1 | 01/10/2019 | 30/09/2023 | Jason Clarkson |
| 2247701 | Studentship | EP/S023151/1 | 01/10/2019 | 30/09/2023 | Michael Hutchinson |
| 2283474 | Studentship | EP/S023151/1 | 01/10/2019 | 06/12/2023 | Antoine Meyer |
| 2247906 | Studentship | EP/S023151/1 | 01/10/2019 | 30/09/2023 | Sahra Ghalebikesabi |
| 2247868 | Studentship | EP/S023151/1 | 01/10/2019 | 30/09/2023 | Jason Clarkson |
| 2635637 | Studentship | EP/S023151/1 | 01/10/2019 | 30/09/2023 | Stefanos Bennett |
| 2283002 | Studentship | EP/S023151/1 | 01/10/2019 | 30/03/2024 | James Wei |
| 2641932 | Studentship | EP/S023151/1 | 01/10/2019 | 31/12/2022 | Melodie Monod |
| 2247869 | Studentship | EP/S023151/1 | 01/10/2019 | 30/09/2023 | Anna Menacher |
| 2282781 | Studentship | EP/S023151/1 | 01/10/2019 | 30/09/2023 | Phillip Murray |
| 2284224 | Studentship | EP/S023151/1 | 01/10/2019 | 30/09/2023 | Chak Hin Bryan Liu |
| 2282778 | Studentship | EP/S023151/1 | 01/10/2019 | 31/03/2024 | Enrico Crovini |
| 2248365 | Studentship | EP/S023151/1 | 01/10/2020 | 30/09/2024 | James Topping |
| 2635642 | Studentship | EP/S023151/1 | 01/10/2020 | 30/09/2024 | Lucile Ter-Minassian |
| 2635641 | Studentship | EP/S023151/1 | 01/10/2020 | 30/09/2024 | Oscar Clivio |
| 2420816 | Studentship | EP/S023151/1 | 01/10/2020 | 30/09/2024 | Lucile Ter-Minassian |
| 2420792 | Studentship | EP/S023151/1 | 01/10/2020 | 30/09/2024 | Desislava Ivanova |
| 2420772 | Studentship | EP/S023151/1 | 01/10/2020 | 30/09/2024 | Andrew Campbell |
| 2635643 | Studentship | EP/S023151/1 | 01/10/2020 | 30/09/2024 | Zoi Tsangalidou |
| 2420820 | Studentship | EP/S023151/1 | 01/10/2020 | 30/09/2024 | Zoi Tsangalidou |
| 2420649 | Studentship | EP/S023151/1 | 01/10/2020 | 30/09/2024 | Oscar Clivio |
| 2632835 | Studentship | EP/S023151/1 | 03/10/2020 | 30/09/2024 | Michael Komodromos |
| 2446052 | Studentship | EP/S023151/1 | 03/10/2020 | 30/09/2024 | Andrea Brizzi |
| 2445745 | Studentship | EP/S023151/1 | 03/10/2020 | 30/09/2024 | Benjamin Howson |
| 2605895 | Studentship | EP/S023151/1 | 03/10/2020 | 30/09/2024 | Jose Folch Urroz |
| 2605897 | Studentship | EP/S023151/1 | 03/10/2020 | 30/09/2024 | Xing Liu |
| 2605900 | Studentship | EP/S023151/1 | 03/10/2020 | 30/09/2024 | Ben Tu |
| 2605902 | Studentship | EP/S023151/1 | 03/10/2020 | 30/09/2024 | Michael Komodromos |
| 2605889 | Studentship | EP/S023151/1 | 03/10/2020 | 30/09/2024 | Alexander larionov |
| 2446166 | Studentship | EP/S023151/1 | 03/10/2020 | 30/09/2024 | Thomas Matcham |
| 2442432 | Studentship | EP/S023151/1 | 03/10/2020 | 30/09/2024 | Tresnia Berah |
| 2564817 | Studentship | EP/S023151/1 | 01/10/2021 | 30/09/2025 | Angus Phillips |
| 2564803 | Studentship | EP/S023151/1 | 01/10/2021 | 30/09/2025 | Max Anderson Loake |
| 2565026 | Studentship | EP/S023151/1 | 01/10/2021 | 30/09/2025 | Nicholas Steyn |
| 2565020 | Studentship | EP/S023151/1 | 01/10/2021 | 30/09/2025 | Vikrant Shirvaikar |
| 2564794 | Studentship | EP/S023151/1 | 01/10/2021 | 30/09/2025 | Joseph Benton |
| 2564812 | Studentship | EP/S023151/1 | 01/10/2021 | 30/09/2025 | Alex Buna Marginean |
| 2602524 | Studentship | EP/S023151/1 | 02/10/2021 | 30/08/2025 | Emmeran Johnson |
| 2602530 | Studentship | EP/S023151/1 | 02/10/2021 | 30/08/2025 | Shahriar kazi |
| 2602507 | Studentship | EP/S023151/1 | 02/10/2021 | 30/08/2025 | Efthymios COSTA |
| 2602755 | Studentship | EP/S023151/1 | 02/10/2021 | 30/08/2025 | Yu Chen |
| 2602756 | Studentship | EP/S023151/1 | 02/10/2021 | 30/08/2025 | Quiquan Wang |
| 2602754 | Studentship | EP/S023151/1 | 02/10/2021 | 30/08/2025 | Fabio Feser |
| 2602749 | Studentship | EP/S023151/1 | 02/10/2021 | 30/08/2025 | Yijin Zeng |
| 2740734 | Studentship | EP/S023151/1 | 01/10/2022 | 30/09/2026 | Nicolas Petit |
| 2740612 | Studentship | EP/S023151/1 | 01/10/2022 | 30/09/2026 | Deepak Badarinath |
| 2740715 | Studentship | EP/S023151/1 | 01/10/2022 | 30/09/2026 | Samuel Howard |
| 2740634 | Studentship | EP/S023151/1 | 01/10/2022 | 30/09/2026 | Stefano Cortinovis |
| 2740743 | Studentship | EP/S023151/1 | 01/10/2022 | 30/09/2026 | Jeffrey Tse |
| 2740724 | Studentship | EP/S023151/1 | 01/10/2022 | 30/09/2026 | George Hutchings |
| 2740759 | Studentship | EP/S023151/1 | 01/10/2022 | 30/09/2026 | Linying Yang |
| 2740739 | Studentship | EP/S023151/1 | 01/10/2022 | 30/09/2026 | Anya Sims |
| 2740638 | Studentship | EP/S023151/1 | 01/10/2022 | 30/09/2026 | Alexander Forster |
| 2748969 | Studentship | EP/S023151/1 | 03/10/2022 | 30/09/2026 | Hetvi Jethwani |
| 2748915 | Studentship | EP/S023151/1 | 03/10/2022 | 30/09/2026 | Guiomar Pescador Barrios |
| 2748829 | Studentship | EP/S023151/1 | 03/10/2022 | 30/09/2026 | Marcos Tapia Costa |
| 2748724 | Studentship | EP/S023151/1 | 03/10/2022 | 30/09/2026 | Joshua Corneck-Willcox |
| 2749396 | Studentship | EP/S023151/1 | 03/10/2022 | 30/09/2026 | Pavithra Srinath |
| 2748527 | Studentship | EP/S023151/1 | 03/10/2022 | 30/09/2026 | Brendan Martin |
| 2891754 | Studentship | EP/S023151/1 | 01/10/2023 | 30/09/2027 | DA Shozen |
| 2886777 | Studentship | EP/S023151/1 | 01/10/2023 | 30/09/2027 | Marcel Hedman |
| 2886365 | Studentship | EP/S023151/1 | 01/10/2023 | 30/09/2027 | Kianoosh Ashouritaklimi |
| 2886732 | Studentship | EP/S023151/1 | 01/10/2023 | 30/09/2027 | Peter Potaptchik |
| 2891773 | Studentship | EP/S023151/1 | 01/10/2023 | 30/09/2027 | Toby Boyne |
| 2891756 | Studentship | EP/S023151/1 | 01/10/2023 | 30/09/2027 | PAULA Cordero Encina |
| 2891686 | Studentship | EP/S023151/1 | 01/10/2023 | 30/09/2027 | Anya Iskakova |
| 2891802 | Studentship | EP/S023151/1 | 01/10/2023 | 30/09/2027 | Shavindra Jayasekera |
| 2886709 | Studentship | EP/S023151/1 | 01/10/2023 | 30/09/2027 | Leo Zhang |
| 2891705 | Studentship | EP/S023151/1 | 01/10/2023 | 30/09/2027 | Paul Valsecchi Oliva |
| 2886852 | Studentship | EP/S023151/1 | 01/10/2023 | 30/09/2027 | Qinyu Li |
| 2886858 | Studentship | EP/S023151/1 | 01/10/2023 | 30/09/2027 | Rafaël Brutti |
| 2891663 | Studentship | EP/S023151/1 | 01/10/2023 | 30/09/2027 | Vanessa Madu |
| 2891741 | Studentship | EP/S023151/1 | 01/10/2023 | 30/09/2027 | Keyi Jiang |
| 2891767 | Studentship | EP/S023151/1 | 01/10/2023 | 30/09/2027 | REBECCA Langdon |
| 2886723 | Studentship | EP/S023151/1 | 01/10/2023 | 30/09/2027 | Jack Foxabbott |