Starts 28 Feb 2019 11:00
Ends 28 Feb 2019 12:00
Central European Time
Leonardo Building - Luigi Stasi Seminar Room
Mathematical and statistical modelling of complex systems is one of the key methods to understand and master their complexity. In this talk, I will first show how Bayesian machine learning can be combined with mathematical logic (specifically, temporal and spatio-temporal logic) to provide effective tools to analyse the behaviour of complex systems, unveiling their emergent properties, and to tackle system design parameter synthesis, and model identification. In the second part of the talk, I will discuss some work in progress, extending the previous ideas to tackle (related) problems like: automatic learning of model abstractions, using also Deep Neural Networks, with applications in multiscale modelling in systems biology and in geophysics, and learning of logic-based classifiers in the context of explainable Al.