RBI Scientists Became Partners in the Best Ranking EU Project
The project, which was launched under a special FET Xtrack contest of the Seventh Framework Programme (FP7), brings together top laboratories in the field of information and communication technologies in five European countries. Over the next three years this team of experts will work on developing a new generation of machine learning algorithms. The potential applications of these new algorithms cover diverse areas such as molecular biology, cellular telephony, sensor and smart energy networks, multimedia and social networks.
The MAESTRA project ranked first on the EU funding shortlist
''It is important to emphasize that MAESTRA project received excellent evaluation by the European Commission. In fact, MAESTRA project is ranked first on the shortlist of only eight projects selected for EU funding of the total 318 applications. FET (Future and Emerging Technology) projects are a special category of projects in the European Union, expected to enable new scientific and technological breakthroughs so we are extremely pleased to be partners in this EU project.’’ - said Tomislav Šmuc, the co-ordinator of the project at the RBI.
Daily use of computers in all areas of human activity, constant production of even bigger databases as well as stronger and inevitable Internet connection lead to the accumulation of huge amounts of data. Naturally, due to this explosive growth of data all around us, now more than ever we need machines to assist us in the analysis of these data. That is why researchers are constantly working on developing new generations of algorithms for data processing, while facing increasing challenges such as high-dimensional data (where each example is described with a vast multitude of features), incompletely or partially labelled data, as well as data placed in a spatio-temporal or network context.
Developing the Next-Generation of Machine Learning Algorithms
The MEASTRA project aims at developing the new generation of machine learning algorithms that will be able to efficiently create more accurate predictive and descriptive models capable of simultaneously addressing several (ultimately all) of the above complexity aspects.
The next generation of algorithms to be developed under the MAESTRA project could have transformational impact on important aspects of society, such as personalized medicine and social media.
The RBI research team, Fran Supek, Dragan Gamberger and Tomislav Šmuc from the RBI Division of Electronics will play a significant role in the implementation of new algorithms to address the contemporary problems in molecular biology, such as predicting the gene function or relating the composition of microbiota (bacterial microorganisms, such as those found in the gut or skin) to human health.
It is therefore undoubtedly that some of the applications of new models of machine learning developed under the MAESTRA project could have a transformational impact on important aspects of society, such as personalized medicine and social media. At the same time, the project should contribute to the development of computer science by developing a new theoretical and practical methodology in the field of artificial intelligence.
International multidisciplinary team
The MAESTRA project is a collaborative project of the Seventh Framework-Programme (FP7) in the area of information and communication technologies. Other than RBI, the project brings together scientists from five partner institutions: Jozef Stefan Institute (Coordinator, Slovenia), INESC TEC (Portugal), The Saints Cyril and Methodius University of Skopje (Macedonia) and Universita degli Studi di Bari 'Aldo Moro (Italy).
The 1.7 million EUR award from the European Union Seventh Framework Programme will be shared by these five research teams and the RBI experts can count on EUR 270,000 from that amount. The awarded funds will enable the RBI team to improve the research potential in manpower through recruitment of young research scientist (a postgraduate student) on the period of three years.
DISCLAIMER: This project has received funding from the European Union’s Seventh Programme for research, technological development and demonstration under grant agreement No. XY. The contents of this publication are the sole responsibility of the RBI PR Office and can in no way be taken to reflect the views of the European Union.