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Details of Grant 

EPSRC Reference: EP/S023445/1
Title: EPSRC Centre for Doctoral Training in Distributed Algorithms: the what, how and where of next-generation data science
Principal Investigator: Maskell, Professor S
Other Investigators:
Alexandrov, Professor V Thiyagalingam, Dr J Devlin, Mr LJ
Zhu, Dr X Pinning, Dr R Spirakis, Professor P
mason, Dr l s
Researcher Co-Investigators:
Project Partners:
Arup Group Ltd Cubica Defence Science & Tech Lab DSTL
Denbridge Marine Limited Featurespace GCHQ
IBM UK Ltd Leonardo UK ltd MBDA
National Crime Agency Ordnance Survey QinetiQ
Renishaw RiskAware Ltd Rolls-Royce Plc (UK)
Schlumberger Shop Direct Home Shopping Limited Sintela
Thales Ltd Unilever Vision4ce
Department: Electrical Engineering and Electronics
Organisation: University of Liverpool
Scheme: Centre for Doctoral Training
Starts: 01 April 2019 Ends: 30 September 2027 Value (£): 4,896,240
EPSRC Research Topic Classifications:
Fundamentals of Computing Information & Knowledge Mgmt
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
07 Nov 2018 EPSRC Centres for Doctoral Training Interview Panel D – November 2018 Announced
Summary on Grant Application Form
This CDT will train a cohort of 60 students to have the skills and experience that enables them to become leaders in Distributed Algorithms: capitalising on "Future Computing Systems" to move "Towards a Data-Driven Future".

Commodity Data Science is already pervasive. This motivates today's pressing need for highly-trained data scientists. This CDT will empower tomorrow's leaders of data science. The UK (and world) needs data scientists that can best exploit tomorrow's computational resources to harvest the new 'oil': the information present in data.

As our graduates' careers progress, many cored architectures will become increasingly commonplace. We anticipate millions more cores in tomorrow's desktops than today's. This core count will challenge the assumption made by current Big Data middleware (e.g., Spark and TensorFlow) that the details of future computing systems can be decoupled from the development of data science tools and techniques. More specifically, it will become imperative that data scientists understand how to design algorithms that can operate effectively in environments where data movement is the key performance bottleneck.

To meet this need, we will provide training that ensures we generate highly-employable individuals who have both an understanding of the design of future computer hardware as well as an understanding of how and when to flex the algorithmic solutions to best exploit the computational resources that will exist in the future.

From the outset, the students will be embedded in a computing environment that anticipates the hardware resources that will arrive on their desks after they graduate, not the hardware that exists today. The cohort of students provides the critical mass that motivates engagement with internationally-leading supercomputing centres: STFC's Hartree Centre is an integral part of the team; links we have established with IBM Research in the US will provide students with access to state-of-the-art computing hardware. This anticipation of future computing capability will ensure our graduates are highly employable, but also help motivate end-user organisations to engage with the CDT.

We have identified such end-user organisations that span two themes: defence and security; manufacturing. Organisations in these themes are driven by performance demands and efficiency requirements respectively.

We will align the training we provide with the needs of the cohort, the theme and the individual. Each studentship will have two academic supervisors (one aligned with the "Future Computing Systems" and one aligned with moving "Towards a Data-Driven Future") and at least one supervisor from a project partner. This supervisory team will co-define the scope of each studentship. Once the high quality student has been selected and recruited, we will work with the student to define the training that aligns with their needs and the specific demands of the studentship. Our training provision will include the training needs associated with both the "Future Computing Systems" and "Towards a Data-Driven Future" priority areas. We will use guest lectures from, for example, IBM (as used to train Fast Track civil servants) and UC Berkeley to ensure we maximise our graduates' ability to thrive and to become tomorrow's leaders in Distributed Algorithms.

Key Findings
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Potential use in non-academic contexts
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Date Materialised
Sectors submitted by the Researcher
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Further Information:  
Organisation Website: http://www.liv.ac.uk