Projet TraDE-OPT (Horizon 2020, appel à projets 2019)
Action : Doctoral Networks
Panel : Mathematics
Statut du projet : SIGNED

The main goal of TraDE-Opt is the education of 15 experts in optimization for data science, with a solid multidisciplinary background, able to advance the state-of-the-art. This field is fast-developing and its reach on our life is growing both in pervasiveness and impact. The central task in data science is to extract meaningful information from huge amounts of collected observations. Optimization appears as the cornerstone of most of the theoretical and algorithmic methods employed in this area. Indeed, recent results in optimization, but also in related areas such as functional analysis, machine learning, statistics, linear algebra, signal processing, systems and control theory, graph theory, data mining, etc. already provide powerful tools for exploring the mathematical properties of the proposed models and devising effective algorithms. Despite these advances, the nature of the data to be analyzed, that are “big”, heterogeneous, uncertain, or partially observed, still poses challenges and opportunities to modern optimization. The key aspect of the TraDE-Opt research is the exploitation of structure, in the data, in the model, or in the computational platform, to derive new and more efficient algorithms with guarantees on their computational performance, based on decomposition and incremental/stochastic strategies, allowing parallel and distributed implementations. Advances in these directions will determine impressive scalability benefits to the class of the considered optimization methods, that will allow the solution of real world problems. To achieve this goal, we will offer an innovative training program, giving a solid technical background combined with employability skills: management, fund raising, communication, and career planning skills. Integrated training of the fellows takes place at the host institute and by secondments, workshops, and schools. As a result, TraDE-Opt fellows will be prepared for outstanding careers in academia or industry.

Mots clés : convex optimization, large scale optimization, first order splitting methods, image processing, regularization approaches, incremental and stochastic algorithms, parallel and distributed computations

Participants :
- University of Graz (Autriche) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 528414.48 €
- Université Catholique de Louvain (Belgique) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 512640.0 €
- CentraleSupélec (France) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 549604.08 €
- Camelot Biomedical Systems (Italy) (Italie) — Partner, beneficiary, Organismes privés (hors enseignement), subvention : 261499.68 €
- Polish Academy of Sciences (Pologne) — Partner, beneficiary, Recherche, subvention : 454957.2 €
- Technische Universität Braunschweig (Allemagne) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 505576.8 €
- University of Genoa (Italie) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 522999.36 €
- Polytechnic University of Bucharest (Roumanie) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 439182.72 €