ABSTRACT:
As a first issue, during this talk I will discuss the framework of inverse problems: what they are, why they are difficult to solve and what are the possible strategies for solving them. Then I will focus on Bayesian tracking as a statistical inversion method for solving dynamical inverse problems. As a final step, I will present some results obtained applying a Bayesian Tracking algorithm to real data recorded using Magnetoencephalography.
BIO:
I am a postdoctoral researcher in the Neuroinformatics research group. I received my PhD in Mathematics and Application from the University of Genova, Italy on April 2010. I spent six months as postdoc at the Department of Mathematics, University of Genova. During my PhD I dealt with the study of the solution of inverse problems for infering information about the brain activity from neurophysiological data.