I am a PhD student in the Empirical Inference department, supervised by Bernhard Schölkopf.
My work focuses on the development of unsupervised learning and causal inference methods.
In particular, I work on the theory of novel linear and nonlinear models for independent component analysis, as well as on novel estimation techniques.
I am broadly interested in causality, and how causal modeling and reasoning help interpreting and understanding real world phenomena. Additionally, I am interested in exploring connections between machine learning and causality.
I am also interested in statistical physics. During my master, I took part in an international program on the Physics of Complex Systems involving institutes located in Trieste (SISSA and ICTP), Turin (Politecnico di Torino) and Paris (Universities Pierre & Marie Curie, Paris Diderot, Paris-Sud and the École Normale Supérieure at Cachan).
MultiView ICA for modeling shared responses in neuroimaging
Group studies involving large cohorts of subjects are important to draw general and valid statements about the brain functional organization. However, the successful aggregation of data coming from multiple subjects is challenging, since it requires accounting for large variability in anatomy, functional topography and stimulus response across the individuals.
MultiView independent component analysis methods provide a flexible framework to perform joint feature extraction from groups of subjects, and lend themselves to a principled and expressive modeling of the shared response, while accounting for inter-subject variability.
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