Speaker: Luca Bortolussi, University of Trieste
Stochastic dynamical models on large networks can often only be simulated, typically at a large computational cost. When we are interested in understanding behaviours of the model - described in some formal language - we can rely on simulations to estimate their probability under the model. Doing this under parametric uncertainty, a common situation, may be infeasible. We will present how to leverage Bayesian Machine Learning to get an efficient and accurate estimate of how behavioural probabilities change with model parameters, with applications in parameter synthesis and model design.
Since November 2015, I am Associate Professor of Computer Science at the Department of Mathematics and Geosciences of the University of Trieste, Italy. I am also guest professor of modelling and simulation at the department of Computer Science of the University of Saarland in Saarbruecken, Germany, where I worked from June 2014 to May 2015. Before that, I was assistant professor (Ricercatore) of Computer Science at the Department of Mathematics and Geosciences of the University of Trieste, Italy. I graduated in Mathematics at the University of Trieste in 2003 and got a PhD in Computer Science form the University of Udine in 2007. I have been an honorary fellow of the School of Informatics of the University of Edinburgh from 2013 to 2016, where I spent a sabbatical year in 2012. From 2012 to 2017 I was also associate researcher at ISTI-CNR in Pisa. My research interest span from modelling and simulation of complex systems, to quantitative formal methods, machine learning, and computational systems biology.