Simone Silvetti

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Short Bio

Since 2014 I work in Esteco, a software-house company located in the Area Science Park of Trieste (Italy). I am also finishing my PhD in computer science at the university of Udine under the supervision of Prof. Alberto Policriti and Prof. Luca Bortolussi. My research focuses on the application of machine learning techniques to formal verification of dynamical systems (details from PhD yearly report 2016) . In 2013 I won a scholarship funded by the Area Science Park and I started to work in the numerical methods group of Esteco where I am currently employed. In that period I studied and developed classifier algorithm (mainly Support Vector Machine) which was then implemented in modeFrontier (the core software developed by Esteco). I graduated in 2013 at the University of Rome "La Sapienza" in mathematics. My dissertation was on mathematical physics.

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Research Interests

My research is currently focused on the joint application of quantitative formal methods and machine learning techniques to Verification and Model-based Testing of Complex Systems. I am trying to apply those techniques in the industrial field (mainly Automotive): the idea consists in using Active Learning techniques to test the satisfiability of a set of requirements which could be expressed by using a Temporal Logic.  I am also interested in the modeling of Cyber-Physical-Systems and Internet of Things devices which are going to be of paramount importance in the near future. I have also worked on multi-objective optimization (Genetic Algorithms and Bayesian optimization), surrogates models (Gaussian Processes, Kriging, etc. ), clustering and classification algorithms.



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Teaching Activities

PhD Activities

Work Activities

I am employed in the Numerical Methods Group of Esteco. We take care of the mathematical algorithms of  the software developed by the company. It means maintainability, implementation and developing of new prototypes. We participate to different international conferences and contribute to the industrial research community.


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