Descripción del proyecto
Formación de expertos en optimización para dar sentido a los datos masivos
El objetivo de la ciencia de datos es obtener conclusiones mediante la extracción de información relevante de cantidades ingentes de datos recopilados. El campo de la ciencia de datos incluye el análisis de datos, el análisis predictivo, la extracción de datos y el aprendizaje automático. La optimización constituye la piedra angular de la mayoría de los métodos teóricos y algorítmicos empleados en esta área. El proyecto financiado con fondos europeos TraDE-OPT abordará los retos que plantea el análisis de datos masivos, heterogéneos, variables o parciales. En concreto, el proyecto formará a quince expertos en optimización para la ciencia de datos. El programa de formación ofrecerá una sólida formación técnica combinada con capacidades para el empleo: gestión, recaudación de fondos, comunicación y planificación de la carrera profesional.
Objetivo
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.
Ámbito científico
- 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
Palabras clave
Programa(s)
Régimen de financiación
MSCA-ITN - Marie Skłodowska-Curie Innovative Training Networks (ITN)Coordinador
16126 Genova
Italia