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Training Data-driven Experts in OPTimization

Project description

DEENESFRITPL

Training experts in optimization to make sense of ‘big’ data

The goal of data science is to draw conclusions by extracting meaningful information from huge amounts of collected observations. The field of data science includes data analysis, predictive analytics, data mining and machine learning. Optimisation appears as the cornerstone of most of the theoretical and algorithmic methods employed in this area. The EU-funded TraDE-OPT project will address the challenges involved in analysing data that are ‘big’, heterogenous, uncertain or partially observed. Specifically, the project will train 15 experts in optimisation for data science. The training programme will offer a solid technical background combined with employability skills: management, fund raising, communication, and career planning.

Objective

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.

Fields of science

Programme(s)

Topic(s)

Call for proposal

H2020-MSCA-ITN-2019

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Funding Scheme

MSCA-ITN - Marie Skłodowska-Curie Innovative Training Networks (ITN)

Coordinator

UNIVERSITA DEGLI STUDI DI GENOVA
Net EU contribution
€ 522 999,36
Address
Via balbi 5
16126 Genova
Italy

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Region
Nord-Ovest Liguria Genova
Activity type
Higher or Secondary Education Establishments
Links
Other funding
€ 0,00

Participants (7)

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EC signature date 27 March 2020
Last update: 5 April 2023

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Permalink: https://cordis.europa.eu/project/id/861137

European Union, 2023