Guido Sanguinetti

"Machine Learning Methods in Statistical Model Checking and System Design"

(Vortrag im Rahmen der "MPI Distinguished Lecture Series" in Kooperation mit dem Fachbereich Informatik)

Abstract: Recent research has seen an increasingly fertile convergence of ideas from machine learning and formal modelling. Here we review some recently introduced methodologies for model checking and system design/parameter synthesis for logical properties against stochastic dynamical models. The crucial insight is a regularity result which states that the satisfaction probability of a logical formula is a smooth function of the parameters of a CTMC. This enables us to select an appropriate class offunctional priors for Bayesian model checking and system design. We give a tutorial introduction to the statistical concepts, as well as an illustrative case study which demonstrates the usage of a newly-released software tool, U-check, which implements these methodologies.

Bio: Guido Sanguinetti is a Reader in Machine Learning in the Institute for Adaptive and Neural Computation at the School of Informatics, University of Edinburgh. His interests focus on probabilistic modelling of biological systems, with particular emphasis on inference in dynamical systems.