Seminario: Learning and designing stochastic systems from qualitative properties

Contatto di riferimento: Prof. Emanuele D. Giordano

Recapito telefonico per contatti: +39 0547 339243

About the speaker

Guido Sanguinetti is a Reader in Informatics at the University of Edinburgh, UK. His interests focus on machine learning methodologies for complex systems, in particular dynamical biological systems. He has published more than 60 research articles in leading international journals including Science, PNAS, Nature Communications. He is in receipt of an ERC Starting Grant and was awarded the 2012 PNAS Cozzarelli Prize in Engineering and Applied Science.

Abstract

Stochastic processes are widely used mathematical models in disciplines ranging from biology to physics and economics. Consequently, there has been considerable interest in the statistics and machine learning communities in devising approximate Bayesian inference methods for specific classes of stochastic processes. The general scenario considered is that the data consists of noisy observations of the state of the system at discrete time points. While this is clearly an important scenario, I will argue that it is natural to also consider another type of observations which globally characterise trajectories of the system. These "phenotypic" observations are naturally expressed as constraints which must hold for a continuous subset of the observation interval, i.e. they are "continuous time observations". I will illustrate the problem of estimating model parameters from such observations and the converse problem of designing systems which satisfy (with high probability) such constraints. I will present a generic solution to both problems based on Bayesian optimisation.