Explore the words cloud of the NeEDS project. It provides you a very rough idea of what is the project "NeEDS" about.
The following table provides information about the project.
Coordinator |
COPENHAGEN BUSINESS SCHOOL
Organization address contact info |
Coordinator Country | Denmark [DK] |
Total cost | 1˙168˙400 € |
EC max contribution | 1˙168˙400 € (100%) |
Programme |
1. H2020-EU.1.3.3. (Stimulating innovation by means of cross-fertilisation of knowledge) |
Code Call | H2020-MSCA-RISE-2018 |
Funding Scheme | MSCA-RISE |
Starting year | 2019 |
Duration (year-month-day) | from 2019-01-01 to 2022-12-31 |
Take a look of project's partnership.
# | ||||
---|---|---|---|---|
1 | COPENHAGEN BUSINESS SCHOOL | DK (FREDERIKSBERG) | coordinator | 257˙600.00 |
2 | UNIVERSIDAD DE SEVILLA | ES (SEVILLA) | participant | 322˙000.00 |
3 | KATHOLIEKE UNIVERSITEIT LEUVEN | BE (LEUVEN) | participant | 303˙600.00 |
4 | THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD | UK (OXFORD) | participant | 248˙400.00 |
5 | GEOGRAFIA APLICADA SL | ES (SEVILLA) | participant | 9˙200.00 |
6 | AGEAS SA | BE (BRUXELLES) | participant | 4˙600.00 |
7 | CENTRAAL BUREAU VOOR DE STATISTIEK | NL (DEN HAAG) | participant | 4˙600.00 |
8 | DANMARKS STATISTIK | DK (KOBENHAVN) | participant | 4˙600.00 |
9 | INFORMATICA EL CORTE INGLES SA | ES (MADRID) | participant | 4˙600.00 |
10 | REPSOL SA | ES (MADRID) | participant | 4˙600.00 |
11 | TESCO STORES LIMITED | UK (WELWYN GARDEN CITY) | participant | 4˙600.00 |
12 | BANCO DEL ESTADO DE CHILE | CL (SANTIAGO) | partner | 0.00 |
13 | DUKE UNIVERSITY | US (DURHAM NC) | partner | 0.00 |
14 | UNIVERSIDAD DE CHILE | CL (SANTIAGO) | partner | 0.00 |
NeEDS responds to the massive scientific and technological challenges that the very rapidly growing field of Data Science has created for users and producers of data in Europe and world-wide. The challenges stem from the complexity of the data, the completely novel questions posed to data scientists, as well as the need of non-experts to visualize and interact with the knowledge extracted from data in order to aid data-driven decision-making. Companies and public sector bodies around Europe find they cannot build up the required capabilities quickly enough, and Europe is remarkably behind US academia in increasing Data Science capacity.
NeEDS provides an integrated modelling and computing environment that facilitates data analysis and data visualization to enhance interaction. NeEDS brings together an excellent interdisciplinary research team that integrates expertise from three relevant academic disciplines, Mathematical Optimization, Visualization and Network Science, and is excellently placed to tackle the challenges. NeEDS develops mathematical models, yielding results which are interpretable, easy-to-visualize, and flexible enough to incorporate user knowledge from complex data. These models require the numerical resolution of computationally demanding Mixed Integer Nonlinear Programming formulations, and for this purpose NeEDS develops innovative mathematical optimization based heuristics.
NeEDS consists of four academic beneficiaries, eight industrial beneficiaries (from industry sectors ranging from energy, retailing, insurance to banking, as well as national statistical offices), two academic partners and one industrial partner from five EU countries, USA and Latin America with strong and complementary expertise. With this composition, NeEDS is in a unique position to deliver cutting-edge multidisciplinary research to advance academic thinking on Data Science in Europe, and to improve the Data Science capabilities of industry and the public sector.
NeEDS website | Websites, patent fillings, videos etc. | 2020-02-12 16:52:55 |
Take a look to the deliverables list in detail: detailed list of NeEDS deliverables.
year | authors and title | journal | last update |
---|---|---|---|
2019 |
Sandra BenÃtez-Peña, Peter Bogetoft, Dolores Romero Morales Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach published pages: 102068, ISSN: 0305-0483, DOI: 10.1016/j.omega.2019.05.004 |
Omega | 2020-02-06 |
2019 |
Rafael Blanquero, Emilio Carrizosa, Cristina Molero-RÃo, Dolores Romero Morales Sparsity in optimal randomized classification trees published pages: , ISSN: 0377-2217, DOI: 10.1016/j.ejor.2019.12.002 |
European Journal of Operational Research | 2020-02-06 |
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The information about "NEEDS" are provided by the European Opendata Portal: CORDIS opendata.