Descrizione del progetto
Formazione di esperti in ottimizzazione per dare un senso ai megadati
L’obiettivo della scienza dei dati è trarre conclusioni estraendo informazioni significative da enormi quantità di osservazioni raccolte. Il campo della scienza dei dati comprende l’analisi dei dati, l’analisi predittiva, l’estrazione dei dati e l’apprendimento automatico. L’ottimizzazione appare come la pietra angolare della maggior parte dei metodi teorici e algoritmici impiegati in questo settore. Il progetto TraDE-OPT, finanziato dall’UE, affronterà le sfide legate all’analisi di megadati eterogenei, incerti o parzialmente osservati. In particolare, il progetto formerà 15 esperti in ottimizzazione per la scienza dei dati. Il programma di formazione offrirà un solido background tecnico combinato con competenze utili per l’occupabilità: gestione, raccolta fondi, comunicazione e pianificazione della carriera.
Obiettivo
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.
Campo scientifico
- natural sciencesmathematicspure mathematicsalgebralinear algebra
- natural sciencescomputer and information sciencesdata sciencedata mining
- natural sciencesmathematicspure mathematicsmathematical analysisfunctional analysis
- natural sciencesmathematicspure mathematicsdiscrete mathematicsgraph theory
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
MSCA-ITN - Marie Skłodowska-Curie Innovative Training Networks (ITN)Coordinatore
16126 Genova
Italia