I am a PhD student, working under the joint supervision of Bernhard Schoelkopf, Klaus Scheffler and Gabriele Lohmann.
My work focuses on the application of Machine Learning techniques to the study of the Brain, in particular to fMRI data.
Functional Magnetic Resonance Imaging (fMRI) allows to measure changes in blood flow within the brain. In particular, BOLD (Blood-oxygen-level dependent) fMRI leverages on the changes in relative concentrations of oxyhemoglobin and deoxyhemoglobin in regions where neurons are being activated.
fMRI measurements constitute therefore a proxy of the underlying neural activity, the signal of interest for neuroscientists. However, disentangling it from multiple other confounding factors (physiological noise, experimental noise, etc), is an extremely challenging task.
In my work, I try to investigate what is signal and what is noise in fMRI recordings, and to identify recurrent spatio-temporal patterns within the data.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems