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Details of Grant 

EPSRC Reference: EP/S023151/1
Title: EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning
Principal Investigator: Gandy, Professor A
Other Investigators:
Sejdinovic, Professor D Doucet, Professor A Rousseau, Professor J
Filippi, Dr SL Misener, Dr R Flaxman, Dr S
Rebeschini, Professor P
Researcher Co-Investigators:
Project Partners:
ACEMS African Inst for Mathematical Sciences AIMS Rwanda
Albora Technologies Amazon ASOS Plc
Babylon Health BASF Bill and Melinda Gates Foundation
Bocconi University BP Carnegie Mellon University
Centres for Diseases Control (CDC) Centrica Plc Cervest Limited
Cogent Labs Columbia University Cortexica Vision Systems Ltd
DeepMind Dunnhumby Element AI
Facebook UK Filtered Technologies Harvard University
Heidelberg Inst. for Theoretical Studies Institute of Statistical Mathematics JP Morgan Chase
Leiden University Los Alamos National Laboratory Ludwig Maximilians University of Munich
Manufacturing Technology Centre Mercedes-Benz Grand prix Ltd Microsoft
Novartis Office for National Statistics Prowler.io
Qualcomm Incorporated QuantumBlack Queensland University of Technology
Regents of the Uni California Berkeley RIKEN Samsung
Schlumberger Cambridge Research Limited Select Statistical Services Swiss Federal Inst of Technology (EPFL)
Tencent The Alan Turing Institute The Francis Crick Institute
The Rosalind Franklin Institute UCL UK Atomic Energy Authority
UNAIDS University of British Columbia (UBC) University of Paris 9 Dauphine
University of Washington Vector Institute Winnow Solutions Limited
Department: Dept of Mathematics
Organisation: Imperial College London
Scheme: Centre for Doctoral Training
Starts: 01 April 2019 Ends: 30 September 2027 Value (£): 6,159,464
EPSRC Research Topic Classifications:
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Pharmaceuticals and Biotechnology Energy
R&D Education
Electronics Financial Services
Healthcare Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
07 Nov 2018 EPSRC Centres for Doctoral Training Interview Panel E – November 2018 Announced
Summary on Grant Application Form
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.

Key Findings
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Potential use in non-academic contexts
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Impacts
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Summary
Date Materialised
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Further Information:  
Organisation Website: http://www.imperial.ac.uk