Emerging computing models, software architectures, and intelligent systems

Algorithms and architectures for computation and optimization

Scientific Supervisor: Prof. Michele Flammini

New generation computing: Emerging global communication and service infrastructures are progressively changing the classical view of computing, thanks to the introduction of the social and collaborative platforms typical of web 2.0, to ubiquitous systems, to peer-to-peer networks, and to the distributed access to resources and services in cloud computing systems. This curriculum aims to address the computational aspects related to the efficiency in the use of resources, and in the design and management of services in these systems, resorting on the theory of algorithms and computational complexity, operations research and combinatorial optimization. In addition, it intends to model and analyze the consequences of autonomous users’ behavior on the system performance, integrating algorithmic ideas with techniques borrowed from Economy and Game Theory, in particular for characterizing stable or equilibrium solutions that are consistent with the presence of entities not subject to a centralized control. Finally, an issue of interest concerns the evaluation of the influence of social relationships among users on the overall efficiency of the system.

Software engineering and intelligent systems

Scientific Supervisor: Prof. Vittorio Cortellessa

The research topics tackled in this curriculum can be partitioned in three main areas, that are: Software Architecture, Model-Driven Engineering, Intelligent Systems. Around the concept of Software Architecture, several research topics are taken, which go from automated synthesis of architectural connectors, through functional (e.g. testing, model checking) and non-functional (e.g. performance, reliability) analysis of software, to (run-time) verification and validation. These topics are applied to different domains, such as Service-Oriented and Self-Adaptive Systems. The research on model-driven engineering focuses on different forms of automated co-evolution in modeling ecosystems (e.g. metamodel/models, metamodel/transformations), bidirectionality in model transformations with an emphasis on non-bijective mappings, and model differences/versioning.  Research in the Intelligent Systems Area concerns various fields of Artificial Intelligence and Computational Logic, in particular Artificial Agents (languages and formalisms, Complex Event Processing, run-time self-checking), Agent-based Cognitive Robotics, Learning and Evolving Agents. Advanced applications range from intelligent energy systems to bioinformatics and adaptive control.


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