Alessandro Abate

Date: 17:00, Thursday, February 4, 2016
Speaker: Alessandro Abate
Venue: IST Austria

Abstract:

This talk looks at the development of abstraction techniques based on quantitative approximations, in order to formally verify complex systems and to provide computable approaches for the correct-by-design synthesis of control architectures. The approach employs techniques and concepts from the formal verification area, such as that of (approximate probabilistic) bisimulation, over models and problems from the field of systems and control. While emphasising the generality of the approach over a diverse set of model classes, this talk zooms in on stochastic hybrid systems, which are probabilistic models with heterogeneous dynamics (continuous/discrete, i.e. hybrid, as well as nonlinear). A case study in energy networks, dealing with the problem of demand response, is employed to clarify concepts and techniques. Theory is complemented by algorithms, all packaged in software tools  (called FAUST^2 for stochastic models) that are freely available to users. 

Bio: 
Alessandro Abate received the Laurea degree in electrical engineering in October 2002 from the University of Padova, Padova, Italy, the M.S. degree in May 2004, and the Ph.D. degree in December 2007, both in electrical engineering and computer sciences, from the University of California, Berkeley, CA, USA.
He is an Associate Professor in the Department of Computer Science at the University of Oxford, Oxford, U.K. He has been an International Fellow in the CS Lab at SRI International in Menlo Park, CA, USA, and a Postdoctoral Researcher at Stanford University, Stanford, CA, USA, in the Department of Aeronautics and Astronautics. From June 2009 to mid 2013 he has been an Assistant Professor at the Delft Center for Systems and Control, TU Delft – Delft University of Technology, The Netherlands.
His research interests are in the analysis, verification, and control of probabilistic and hybrid systems, and in their general application over a number of domains, particularly in energy and in systems biology. 
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