The macro-area is composed of two main research areas that are transversal with respect to the methodological approach, attentive to the foundational aspects of research topics deriving from the computational sciences for life. The first area focuses on the study of theoretical and foundational aspects of computation, modeling, and simulation of complex systems and the development of innovative algorithmic methodologies: areas motivated by application contexts such as biology and physics that require the need to define computational foundations. In this context, there are two main specific objectives. The first focuses on the design and experimentation of exact, approximate, evolutionary algorithmic methodologies or machine learning aimed at processing large amounts of data in various application contexts, mainly related to the analysis and integration of biological data. The second objective focuses on the study of computational power and properties of computational models inspired by physical and biological laws, such as membrane systems (or P systems) and reaction systems, with the definition of associated complexity classes and comparison with classical models such as Turing machines and RAM machines, and on the extension and study of specific compositional properties of models, such as Petri nets. The application areas in this context include biology and various aspects of the simulation of complex systems under uncertain conditions, safety and control. The second area of research is in the computational sciences for life and addresses various algorithmic and computational aspects of genomics, transcriptomics and phylogenetics in Bioinformatics, simulation and modelling of processes in computational biology and specifically systems biology. The activities in this area are broad and complementary. The main lines of research active in the field of fundamentals are:
In life sciences and bioinformatics, the main lines of active research are:
Recently is active in the development of algorithms in Fundamentals a new direction of research on Information Security mainly focused on the development and implementation of algorithms and cryptographic protocols.
The area deals with various research topics related to the development of software systems, with particular attention to design, quality control, and maintenance and evolution of software systems. The research activity on software design mainly concerns the definition, development and testing of new architectural models and appropriate methodologies and tools to address emerging application domains. The research activity on software quality mainly concerns the definition of methods for the validation and verification of software systems, the definition of testing techniques, the dynamic and static analysis of programs and the realization of self-healing systems. Finally, research activity on software systems maintenance mainly concerns the definition, development, and testing of reverse engineering techniques for software maintenance and technical debt management. The research activity carried out in this area is wide and varied, the main lines of research in progress are:
The research activities carried out in this macro-area relate to Data Science, and are aimed at developing models and techniques to support the processes of management and analysis of various types of data. In particular, multiple aspects related to the data life cycle are considered, including acquisition, transformation, organization, different types of analysis, knowledge extraction, and interaction with users.
Two main lines of research can be distinguished: the first includes Information Retrieval, Text Mining, and social media analysis, while the second includes the intersection of contiguous and complementary disciplines such as Human-Machine Interaction, Information Systems, Distributed Systems, and Data Semantics. In the first research area, the main interests relate to the definition of models, techniques, and systems aimed at guaranteeing personalized access to information on the Web, the analysis and conceptual representation of texts, the extraction of information from texts (text mining), the analysis of the evolution of information, and the predictive analysis of user-generated content in social media. In the second research line, problems concerning three macro-themes are addressed, often jointly: a) human-machine interaction, with particular attention to human interaction with artifacts, graphic interface design, and data visualization; b) semantic interoperability between systems for data, knowledge and services management, with particular attention to the implementation of open, adaptive and distributed applications in the Cloud through service architectures, semantic integration, and quality analysis; c) value in information systems, with particular attention to the value of information and services. The most active lines of research at this time, include:
Information Retrieval Area:
Area Human-Machine Interaction and Distributed and Semantic Information Systems:
The activity in this area focuses on foundational and industrial research topics covering 1) diverse aspects of the analysis and management of mutimedia and sensorial data of several kinds, 2) robotics, and 3) real-time intelligent sensing. The research activities on topic 1) focuses on the development of algorithms and techniques for analysing and managing digital signals, with special interest on multimedia signals (audio, images, and video), fisiological signals (EEG (electroencefalogram), ECG (electrocardiogram), skin galvanic responses, blood pressure, temperature), and psychofisical signals (eye tracker).
Moreover, it designs and develops novel methods for Computer Vision, Pattern Recognition, Machine Learning, Artificial Intelligence, and Multimedia, applied to diverse types of data with a specialization on managing images, video, and data from multimodal sensors. The research activity on robotics focuses on systems for world-perception by autonomous robots, in particular mapping the working environment, localization, and scene understanding and tracking. The research on real-time systems is developed over conceptual and computational instruments for understanding and controlling complex systems which evolve with time, passing through a series of macroscopic states, each of which is characterised by a specific set of rules which depend on data collected at real-time.
The main active research topics are:
This research area studies research topics traditionally linked to Artificial Intelligence along methods, techniques, models, and applications for decision support. In particular, inference models based on techniques for knowledge representation and management, and statistical inferences. Learning models and algorithms based on structured, semi-structured, and unstructured data are designed and experimented, along with computational methods for optimization problems for data analytics. Also distributed approaches (multi-agent systems) for modelling and simulating complex systems, natural or artificial, are designed.
Within the large spectrum of research challenges associated to this area, the main research lines are: