Marco Scutari: Bayesian Networks, Complex Data and Maximum Entropy.
28 June 2018
Meeting Room @ IDSIA, Galleria 1, 11h00
Most challenging problems in the life sciences, natural sciences and engineering involve disentangling complex networks of probabilistic and causal relationships among heterogeneous, large data sources. This endeavour poses three challenges: devising algorithms that can scale to high dimensions; formulating models that are auditable and interpretable, but also flexible; and balance interpretability against predictive accuracy. Bayesian networks strive to address these concerns, and can be learned efficiently from both big data and high-dimensional data. In this talk I will discuss some of my recent research on computational and information-theoretic aspects of structure learning, touching on the challenges of incomplete and correlated observations and more in general on the nature of prediction in machine
learning models.

The speaker

 Marco studied Statistics and Computer Science at the University of Padova, Italy. He earned his PhD in Statistics in Padova under the guidance of Professor A. Brogini and Professor K. Strimmer, with a dissertation on prior and posterior distributions used in graphical modelling. In 2011, he moved to University College London (UCL) as a Research Associate in Statistical Genetics at the Genetics Institute (UGI), later joining the Department of Statistics in Oxford as a Lecturer in Statistics in 2014. His research focuses on the theory of Bayesian networks and their applications, often to biological data. He is the author and maintainer of the bnlearn R package, and wrote “Bayesian Networks in R with Applications in Systems Biology” for Springer and “Bayesian Networks with Examples in R” for Chapman and Hall.