My research covers five main topics. The first topic is adaptive system architectures. My main contributions in the area are the development of programming abstractions for adaptive control and techniques for observing and analyzing system behavior. My second topic is support for multimedia applications, including continuous media. My main contributions within this topic are applying formal specifications of quality of service management (QoS) to the configuration of the running environment for multimedia applications and the development of explicit binding architectures for multimedia communication. The third theme is support for mobility and context-sensitive systems. This includes observing changes in the environment and offering the best possible service based on these observations (for example in the personalization of services). My main contributions are context-sensitive configuration and reconfiguration based on current context and support for integration of a wide range of services and information resources. My fourth theme is security. My main contributions here are research around adaptable security mechanisms, development of secure and user-friendly storage and sharing of information, combination of secure multiparty computation (SMC) and public key technology to solve problems around privacy and analysis of data (including health data), and security in the context of mobile devices, NFC and secure elements (SIM cards). The fifth main theme is within AI and machine learning (ML). My main contributions here are within system and programming support when implementing solutions that use ML.
Chiara Bordin is Associate Professor with a focus on teaching and research within the Energy Informatics domain.
Her main research interests are related to mathematical optimization in the context of computer science applied to smart energy and power systems. This refers in particular (but is not limited) to: models for storage integration and storage technologies assessment; strategic network design, expansion and operation; power systems reliability oriented network restructuring and reconfiguration; multi-agent systems for microgrids coordination; stochastic multihorizon optimization; optimal management of electric vehicles and charging sites; integration of machine learning methodologies in the context of mathematical modelling.
Mathematical modelling and optimization is a strong interdisciplinary and versatile subject. By combining it with computer science, power systems engineering, economics, big data analytics and machine learning, it is possible to address many research questions within the energy and power systems field, enhance analyses and look at the studied systems from different perspectives. This can provide key methodological and analytical contributions to the young and dynamic research area of Energy Informatics, with particular regard to the two major research themes of “smart energy-saving systems” and “smart grids”.
Member of the Arctic Centre for Sustainable Energy (ARC): ARC Website