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Details of Grant 

EPSRC Reference: EP/K009788/1
Title: Network on Computational Statistics and Machine Learning
Principal Investigator: Girolami, Professor M
Other Investigators:
Shawe-Taylor, Professor JS
Researcher Co-investigators:
Project Partners:
DeepMind Technologies Limited Featurespace Healthsolve
IBM Haifa Research Labs Microsoft Research Ltd NCR (Scotland) Ltd
Select Statistical Services Winton Capital Management Ltd. Xerox Research Centre Europe
Department: Statistical Science
Organisation: University College London
Scheme: Network
Starts: 24 June 2013 Ends: 06 January 2014 Value (£): 104,530
EPSRC Research Topic Classifications:
Artificial Intelligence Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:  
Summary on Grant Application Form
The aim of this network is to establish the UK as the world leading authority in the joint area of Computational Statistics and Machine Learning (CompStat & ML) by advancing communication, interchange and collaboration within the UK between the disciplines of Computational Statistics (CompStat) and Machine Learning (ML).

The UK has tremendous research strength and depth that is widely acknowledged as world leading in both the individual areas of Computational Statistics and Machine Learning. Despite each of these fields of research developing, largely, independently and having their own separate journals, international societies, conferences and curricula both areas of investigation share a common theoretical foundation based on the underlying formal principles of mathematical statistics and statistical inference. As such there is a natural diffusion of concepts, research and individuals between both disciplines. This network will seek to formalise as well as enhance this interchange and in the process capitalise on important synergies that will emerge from the combined and shared research agendas of CompStat & ML.
Key Findings
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Potential use in non-academic contexts
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Impacts
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Sectors submitted by the Researcher
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Project URL:  
Further Information:  
Organisation Website: http://www.ucl.ac.uk