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Collegamenti utili

Academic Staff

Francesco Cauteruccio

Email: francesco.cauteruccio[AT]unical.it
Web: Personal site

Sequences Comparison, Complex Network Analysis, Artificial Intelligence, Bioinformatics, Internet of Things.

His research interests lie in different fields, mainly sequences comparison (with applications to bioinformatics and time series analysis), complex network analysis and artificial intelligence. His works have been carried out in collaboration with different groups, such as the CREATIS at Université Claude Bernard 2 (Lyon, France), Technical University of Eindhoven (the Netherlands), the Polytechnic University of Marche (Italy), University of Calabria (Italy) and the Centro Nazionale delle Ricerche (CNR, Italy).

In the context of sequences comparison, research activities mainly focused on the definition and validation of novel sequence similarity metrics, with applications to different contexts. In particular, his works on sequences comparison included: (1) The definition of a framework generalizing identity-based sequence comparison metrics; (2) The introduction of a novel similarity metric called Multi-Parameterized Edit Distance (MPED); (3) The application of the MPED to different contexts, such as Anomaly Detection, White Matter Fiber-bundles Analysis and the investigation of neurological disorders. Due to the proved computational complexity of the MPED, different meta-heuristics and high-performance computation methods have been designed and developed to apply the MPED in the aforementioned contexts.

In the field of complex network analysis, his research activity focused on the definition and analysis of new centrality measures and information diffusion models, with applications to the investigation of neurological disorders (such as Alzheimer’s disease) and Multi Internet of Things (MIoT) respectively. Moreover, in the context of artificial intelligence his works explored the possibility of coupling Machine Learning (ML) and deductive formalisms (such as Answer Set Programming) to overcome limitations of both the approaches, combining rule-based systems with neural networks.


Teaching - Academic Year 2019/2020: