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Research Lines

Cognitive Computing

Cognitive Computing deals with systems that naturally learn and interact with humans in complex environments. 

This research line has its roots in Artificial Intelligence and includes technologies for knowledge, natural language, vision, audio, and person-machine interaction. 

The scientific challenges include:

  • the search for paradigms of machine learning that integrate and synthesize multi-modal representations based on different inputs (sensor data, unstructured information and structured knowledge). 
  • the search for person-machine interaction paradigms that take into account both the variability of the context of the interaction and the evolution of the participants in the interaction.

Cognitive Computing intends to bring a significant technological advancement, moving from a task-oriented approach - in which the system solves simple and partial tasks - to application scenarios in which the system has more knowledge of the context in which it operates and supports humans in complex activities, or even works completely autonomously.

Complex Data Analytics

Complex Data Analytics deals with systems able to analyze big data and to transform the restless streams of data in value, knowledge and decision capability.

This research line includes research on predictive models, socio-technical systems, and analysis of social and environmental phenomena. It is characterized by the ability:

  • to build predictive models learned automatically from sample data or by accurate mathematical models and to integrate complex data flows
  • to develop solutions of scientific computing, including cloud and web interfaces for big data, spatial data with GIS and webGIS, even on huge datasets.

Adaptive Reliable and Secure Systems

Adaptive, Reliable and Secure Systems deals with reliable and secure systems that operate in open, distributed, dynamic and unpredictable environments.

This research line includes software engineering, embedded systems, and information security.
The scientific challenges include :

  • the production of scientific results focused on specific areas such as requirements, modeling, architecture, verification and validation, security, usability, adaptability and intelligence systems
  • the development of a new integrated approach able to support the whole cycle of design and implementation of complex systems built on hardware and software components interconnected via network.