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Project MAESTRA concluded with rating of Excellent

European project MAESTRA (Learning from Massive, Incompletely annotated, and Structured Data), which had the collaboration of the Laboratory of Artificial Intelligence and Decision Support (LIAAD) of INESC TEC, received a final rating of Excellent.

Focused on the area of information and communication technologies, MAESTRA had the goal of developing tools and methods for predictive learning tasks. The predictive modelling methods developed in this project handle complex and structured data flows, generated by non-stationary processes with a high degree of uncertainty. Several algorithms for classification problems and for regression in problems were developed with the ultimate goal of predicting complex structures: vectors, sequences or graphs. These methods have potential and utility in different problems and in a wide range of areas (molecular biology, sensor networks, multimedia, and social media).

The team from INESC TEC coordinated the WP2 – Methods for Structured Output Prediction from Data Streams and contributed significantly to the WP3 – Methods for analysis of network data, and to the WP4 - Applications of the developed methods. The team from LIAAD was coordinated by João Gama and included researchers João Mendes Moreira, Carlos Ferreira, João Duarte, Rita Paula Ribeiro, Ricardo Sousa, Shazia Tabassum and Luís Matias.

MAESTRA is a collaborative project that includes, other than INESC TEC, the Institut Jozef Stefan (Slovenia), the SS Cyril and Methodius University (Macedonia), the Universita Degli Studi di Bari “Aldo Moro” (Italy) and the Ruder Boskovic Institute (Croatia).



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