The group investigates structural aspects of fundamental problems in Computer Science and their mathematical models. This leads to the design of better algorithms protocols and systems as well as understanding of their implicit computational limits. Specific areas of interests include: algorithm design, data structures, string algorithms, computational complexity, combinatorial optimization, coding and information theory, machine learning. Most results obtained are in the intersection of algorithmics with several other areas in theory and applications, including bioinformatics, communication networks, operating research and artificial intelligence.
Application of theoretical methods and data analysis to model information underlying biological processes: graph and string algorithms for systems biology; advanced data structures for sequence data; distance measures for biological sequences; natural (biotechnological, membrane) computing; pattern recognition, machine learning for biomedical data.
The group conducts research in Automated Reasoning: theorem proving,
decision procedures for satisfiability, satisfiability modulo
theories/assignments, model building, rewriting, and applications.
The Biomedical Imaging Group is active in Experimental and Translational Biomedical Imaging, with main focuses on Magnetic Resonance and Optical Imaging. The lab is equipped with a MRI Tomograph (Bruker, Biospec 4.7 T) and with an Optical Images (IVIS Spectrum, PerkinElmer).
This research group deals with several topics related to the development and the application of the emerging blockchain technology and the smart contract infrastructure.
Il gruppo si occupa di attività di ricerca nel campo del calcolo delle variazioni, teoria geometrica della misura, teoria del controllo ottimo, teoria del trasporto ottimo, e applicazioni.
Development of advanced theoretical and computational mathematical methods for transport and diffusion phenomena in complex systems, multivariate approximation and high-dimensional control problems.
The aim of the research group is to apply electronic design automation (EDA) techniques to cyber-physical production systems (CPPSs) and Industrial IoT (IIoT) for modeling, simulation, synthesis and testing of production lines.
Research activities in this field concern the History of Physics, particularly devoted to the XX century, and Physics Education following active learning methods and in sinergy with the History of Physics with reference to the Foundations of Physics and to the Nature of Science, particularly devoted to Physics teaching at University level and to perspective primary and secondary school teachers.
The aim of the research group is to apply formal methods to modelling, verification and synthesis of engineering systems. The domains range from timed systems to nonlinear cyberphysical systems.
The group conducts research in Artificial Intelligence, including Automated Reasoning, Search Algorithms, Knowledge Representation, Machine Learning, Multi-Agent Systems, and their applications.
The lab has is core business on the preparation, fabrication and characterization of second generation thin film solar cells.
CdTe thin film solar cells with efficiencies exceeding 15% have been fabricated, also flexible solar cells deposited on polymer foils with more than 10% efficiency have been made.
The laboratory is equipped with many different vacuum deposition machines, and electrical and morphological characterization instruments.
The laboratory has also a new different area on the characterization on biological samples and nanostructures by atomic force microscopy.
Design and verification of communication technologies capable of bringing efficiency and sustainability to key applications such as industry, agriculture, building automation, transport and land management.
The aim of the research group is the development and optimization of Software targeting multi-core CPU/many-core GPUs for resource constrained computing platform (e.g., Edge Computing) and for High-performance Computing (HPC) platforms.
Research activities in this field concern applications to interdisciplinary studies in Biotechnology and Cultural Heritage of spectroscopy, microspectroscopy and imaging in the medium and near infrared range and in the X-ray range (XANES and EXAFS), including multivariate statistical analysis of spectral data.
VIPS activities are devoted to the analysis, recognition, modeling and prediction of multivariate multidimensional signals and patterns by artificial intelligence and machine learning techniques. Specific expertise and application domains include image processing, computer vision, pattern recognition, machine learning, human-machine interaction, computer graphics, mixed reality and gaming, analysis and modeling of biomedical and neuroscience data for both basic and translational research.