With the new Data Science Research Area, FBK develops the activities of machine learning, complex networks and deep learning as core competences of the centre and application opportunities in big data and predictive analysis.
Data Science has rapidly become a factor of social and economic transformation: from organizations’ big data to personal sensors’ big data, Data Science has the opportunity to guide the improvement of human and environmental health, providing new tools for knowledge and decision making. Connecting Data Science and AI opens up opportunities for social and economic change, generating new ideas that can change the world and transforming the ability to decide into a continuous process, at low cost and high value, starting with the combination of complex, multimodal and even incomplete data. It is also the time to ensure transparent and reproducible access to these new technologies, decisively orienting research actions towards a “Data Science for Good” and offering opportunities for training aimed at know-how.
In addition to scientific challenges, Data Science research offers new solutions in fields such as health, quality and production in industry and agriculture, environmental safety, development, social networks and public infrastructure. It is necessary for sales organisations (e. g. retail), business strategies, decision-making processes of public bodies. The Data Science Area will therefore contribute with new tools to ICT projects dedicated to vertical domains, will increase FBK’s ability to develop projects of ethical importance and territorial spill-over effects, and will exploit its ability to devise analytics solutions and predictive models for organizations and companies. The theme of deep learning will be specifically at the centre of applied research actions, both for scientific projects and developing new solutions for clouds, sensors and portable devices. A new action will be dedicated to complex multilayer networks, with a junior group to explore aspects of basic research and applications.
As technical challenges, deep learning and geodata science (nowcasting) skills will be brought to the system, developed concepts of predictive analysis associated with dynamic visualization of data, built the link elements between data science and AI in genomic research, developed the theme of predictive maintenance in cross-over with HII Industry 4.0