High-dimensional data processing: from theory to imaging applications

Project Information

HiDDaProTImA

Grant agreement ID: 655282

DOI 10.3030/655282

Project terminated on 31 August 2017

EC signature date 26 March 2015

Start date 1 February 2016

End date 31 January 2018

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost € 183 454,80

EU contribution € 183 454,80

Coordinated by

THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE
United Kingdom

Objective

The unstoppable increase in the volume of data stored, transmitted and interpreted by fixed and mobile devices strongly calls for the study of efficient solutions in processing the information contained in high-dimensional signals. Such need has been reflected in the recent flourishing of research efforts from the statistics, machine learning, computer science and signal processing communities.
Within this multidisciplinary research ground, the proposed project will address the central question that can be formulated as -- what is the maximum level of information contained in large datasets that we can process from a small number of features, and how is it possible to achieve such limit in practice?

Recent advances in information processing have demonstrated that a promising mathematical tool to tackle this question is represented by the Bayesian approach, in which statistical models inferred from training samples accurately describe the data. In fact, the Bayesian framework offers fundamental advantages in modeling high-dimensional signals in terms of mathematical tractability of performance limits as well as enhanced capabilities in information processing.

Beyond the study of performance limits, the proposed project will involve case studies and applications in image processing. The researcher will be able to establish active collaborations with various research groups, in different department of Cambridge University, that test their research results on actual imaging devices.

This project will also form the proposer to his future independent research activity and it will provide him with new mathematical skills and practical implementation expertise with actual imaging systems. On the other hand, Cambridge University will benefit from the cross pollination of ideas brought by the researcher and his collaborators in top institutions in Europe and the US.

Fields of science (EuroSciVoc)

CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.

• natural sciences › computer and information sciences › artificial intelligence › computer vision › facial recognition
• natural sciences › physical sciences › optics › microscopy › electron microscopy
• natural sciences › mathematics › applied mathematics › statistics and probability
• natural sciences › computer and information sciences › data science › data processing
• natural sciences › computer and information sciences › artificial intelligence › computational intelligence

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Keywords

• Big data
• feature extraction
• Bayesian framework
• signal processing
• compressive sensing
• MRI
• microscopy
• spectroscopy

Programme(s)

• H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme
• H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility

Topic(s)

• MSCA-IF-2014-EF - Marie Skłodowska-Curie Individual Fellowships (IF-EF)

Call for proposal

H2020-MSCA-IF-2014

Funding Scheme

MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

Coordinator

THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE

• Net EU contribution: € 183 454,80
• Address: TRINITY LANE THE OLD SCHOOLS ; CB2 1TN Cambridge ; United Kingdom
• Region: East of England › East Anglia › Cambridgeshire CC
• Activity type: Higher or Secondary Education Establishments
• Total cost: € 183 454,80