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NoMADS SIGNED

Nonlocal Methods for Arbitrary Data Sources

Total Cost €

0

EC-Contrib. €

0

Partnership

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Project "NoMADS" data sheet

The following table provides information about the project.

Coordinator
WESTFAELISCHE WILHELMS-UNIVERSITAET MUENSTER 

Organization address
address: SCHLOSSPLATZ 2
city: MUENSTER
postcode: 48149
website: www.uni-muenster.de/en/

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Total cost 1˙111˙500 €
 EC max contribution 1˙111˙500 € (100%)
 Programme 1. H2020-EU.1.3.3. (Stimulating innovation by means of cross-fertilisation of knowledge)
 Code Call H2020-MSCA-RISE-2017
 Funding Scheme /MSCA-RISE
 Starting year 2018
 Duration (year-month-day) from 2018-03-01   to  2022-02-28

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    WESTFAELISCHE WILHELMS-UNIVERSITAET MUENSTER DE (MUENSTER) coordinator 229˙500.00
2    THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE UK (CAMBRIDGE) participant 238˙500.00
3    UNIVERSITEIT TWENTE NL (ENSCHEDE) participant 112˙500.00
4    THE UNIVERSITY OF NOTTINGHAM UK (NOTTINGHAM) participant 81˙000.00
5    UNIVERSITE DE BORDEAUX FR (BORDEAUX) participant 76˙500.00
6    ECOLE NATIONALE SUPERIEURE D'INGENIEURS DE CAEN FR (CAEN) participant 54˙000.00
7    INSTITUTO SUPERIOR TECNICO PT (LISBOA) participant 54˙000.00
8    Ecole Polytechnique of Paris FR (PALAISEAU CEDEX) participant 40˙500.00
9    UNIVERSITA DEGLI STUDI DI GENOVA IT (GENOVA) participant 40˙500.00
10    TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY IL (HAIFA) participant 36˙000.00
11    UNIVERSITE DE CAEN NORMANDIE FR (CAEN CEDEX 5) participant 36˙000.00
12    UNIVERSITE LYON 1 CLAUDE BERNARD FR (VILLEURBANNE CEDEX) participant 31˙500.00
13    CAMELOT BIOMEDICAL SYSTEMS SRL IT (GENOVA) participant 27˙000.00
14    MEDIGUIDE LTD IL (HAIFA) participant 27˙000.00
15    UNIVERSIDAD POMPEU FABRA ES (BARCELONA) participant 22˙500.00
16    POLITECNICO DI MILANO IT (MILANO) participant 4˙500.00
17    ASTRAZENECA UK LIMITED UK (CAMBRIDGE) participant 0.00
18    CLINICAL SCIENCE SYSTEMS NL (LEIDEN) participant 0.00
19    CLK GMBH DE (ALTENBERGE) participant 0.00
20    DATEXIM FR (CAEN) participant 0.00
21    THE MATHWORKS LIMITED UK (CAMBRIDGE CAMBS) participant 0.00
22    CARNEGIE MELLON UNIVERSITY US (PITTSBURGH) partner 0.00
23    THE REGENTS OF THE UNIVERSITY OF CALIFORNIA US (OAKLAND CA) partner 0.00

Mappa

 Project objective

In NoMADS we focus on data processing and analysis techniques which can feature potentially very complex, nonlocal, relationships within the data. In this context, methodologies such as spectral clustering, graph partitioning, and convolutional neural networks have gained increasing attention in computer science and engineering within the last years, mainly from a combinatorial point of view. However, the use of nonlocal methods is often still restricted to academic pet projects. There is a large gap between the academic theories for nonlocal methods and their practical application to real-world problems. The reason these methods work so well in practice is far from fully understood. Our aim is to bring together a strong international group of researchers from mathematics (applied and computational analysis, statistics, and optimisation), computer vision, biomedical imaging, and remote sensing, to fill the current gaps between theory and applications of nonlocal methods. We will study discrete and continuous limits of nonlocal models by means of mathematical analysis and optimisation techniques, resulting in investigations on scale-independent properties of such methods, such as imposed smoothness of these models and their stability to noisy input data, as well as the development of resolution-independent, efficient and reliable computational techniques which scale well with the size of the input data. As an overarching applied theme we focus in particular on image data arising in biology and medicine, which offers a rich playground for structured data processing and has direct impact on society, as well as discrete point clouds, which represent an ambitious target for unstructured data processing. Our long-term vision is to discover fundamental mathematical principles for the characterisation of nonlocal operators, the development of new robust and efficient algorithms, and the implementation of those in high quality software products for real-world application.

 Work performed, outcomes and results:  advancements report(s) 

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