Projet BIGMATH (Horizon 2020, appel à projets 2018)
Action : Doctoral Networks - Industrial Doctorates
Panel : Mathematics
Statut du projet : CLOSED

BIGMATH is aimed to train a group of young, creative mathematicians with strong theoretical and practical skills, needed to tackle the major challenges of the Big Data era. They will also be trained in a wide set of “soft skills” that enable them to transfer effectively their knowledge to the productive world, thus fostering the European market to create innovation. These abilities will result from a close partnership between academy, providing the students with up-to-date training and knowledge on cutting-edge research on targeted mathematical disciplines, and a group of industries, who will complete the competences of the ESRs by exposing them to a set of Big Data-related real industrial problems. The main domains of interest of the BIGMATH project lie in the areas of optimization, statistics, and large-scale linear algebra for Big Data, which are the most relevant mathematical topics for effective machine learning techniques and ability to build good data-driven products. The effectiveness of the training program that we propose strongly relies on the involvement and close collaboration of universities with the non-academic sector, since Big Data challenges cannot be tackled only through theoretical studies and must be identified mostly by companies, which work daily on problems that involve big, complex or “messy” data. Specifically, BIGMATH focuses on 7 industrial Big Data problems spread across three domains: human facial data analysis, financial applications, and production systems. Project Activities and ESRs training on communication, exploitation of scientific results, dissemination and public engagement, play also a central role in this project, since they are designed to promote dissemination of excellent research and diffusion of innovation in Europe. The creation of such international and life-long network of young researchers, trained across sectors in an innovative way, will thus help Europe to strengthen its international R&I cooperation.

Mots clés : big data, network science and learning, features selection, model reduction, statistical geometric methods, large scale linear algebra, distributed optimization, non convex optimization

Participants :
- Credimi spa (Italie) — Partner, beneficiary, Organismes privés (hors enseignement), subvention : 0.0 €
- University of Novi Sad (Serbie) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 217825.56 €
- Sioux lime bv (Pays-Bas) — Partner, beneficiary, Organismes privés (hors enseignement), subvention : 0.0 €
- University of Milan (Italie) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 522999.36 €
- Acomea societa di gestione del risparmio s.p.a. (Italie) — Partner, beneficiary, Organismes privés (hors enseignement), subvention : 0.0 €
- 3lateral doo novi sad (Serbie) — Partner, beneficiary, Organismes privés (hors enseignement), subvention : 0.0 €
- Centar za investicije i finansije doo, beograd (vracar) (Serbie) — Partner, beneficiary, Organismes privés (hors enseignement), subvention : 0.0 €
- Eindhoven University of Technology (Pays-Bas) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 531239.76 €
- University of Lisbon (Portugal) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 475440.48 €
- Uroboptics - technical consulting & research, lda (Portugal) — Partner, beneficiary, Organismes privés (hors enseignement), subvention : 0.0 €
- Sdg consulting italia spa (Italie) — Partner, beneficiary, Organismes privés (hors enseignement), subvention : 0.0 €