The current research of the Group addresses the development of new
Machine Learning methodologies and the analysis of their computational properties.
The research is aimed at defining
frameworks for the development of intelligent systems and intelligent data analysis tools
for learning in structured domains (sequences, trees, graphs)
and for the application to innovative interdisciplinary fields.
The design of learning algorithms includes Neural Networks, Probabilistic models, Reservoir Computing models, Deep Learning approaches, Kernel-based methods, Support Vector Machines, Adaptive Processing
of Structured Data, and other Pattern Recognition techniques.
The application fields include BioInformatics, ChemInformatics, Robotics, Intelligent Wireless Sensor Networks, and Signal/Image Processing. This is particularly important because of the fast growth
of information sources due to the explosion in the use of Internet and Sensor Networks and of the new IT
capabilities that results from the exploitation and integration of different intelligent
The Group has participated in many EU and Italian funded projects.
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- Intelligent Data Analysis
- ChemInformatics (QSPR/QSAR, Toxicology)
- Health/biomedical Informatics
- Robotics and Wireless Sensor Networks
- Signal and Image Analysis
- Intelligent/Adaptive Sensor Networks
- Ambient Assisted Living and Human Activity Recognition
- Parallel Computing
Recent/Current EU Projects
- “Robotics UBIquitous COgnitive Network”, EU FP7, RUBICON [2011-2014]
- Decrease of cOgnitive decline, malnutRition and sedEntariness by elderly
empowerment in lifestyle Management and social Inclusion, EU FP7, DOREMI [2013-2016]
Current National Projects
- SIR-MIUR, LIST-IT:
Learning non-Isomorph Structured Transductions for Image and Text fragments (D. Bacciu) [2015-2018]
- Metodologie informatiche avanzate per l'analisi di dati biomedici, PRA UniPi 2017 [2017-2018]
Recent/Current Industrial Collaborations
- ST Microelectronics, Machine Learning models for industrial process Big Data [2015-2016]
- Biobeats, Machine Learning analysis of biological signals