I am researcher in signal and image processing with interests in the following fields:

Blind source separation and matrix factorization: my research activity first focus on designing new statistical methods to solve Blind Source Separation using sparse signal representations. This also include a deep interest in exploring various applications of these methods to tackle challenges in fields ranging from astrophysics and cosmology to biomedical imaging. These investigations are particularly focused on the analysis of the Planck data from the estimation of the Cosmological Microwave Background to the diffuse components.

Compressed sensing and its applications: part of my investigations has focused on compressed sensing and its applications in astronomy, microscopy and optical sensing.

Machine learning: my research deals with representation learning using matrix factorization and deep learning and its applications in astrophysics. These investigations have been applied to learning data-driven representations galaxy SED and bias correction for shear measurement from weak leasing surveys.

Optimisation and algorithmic tools for inverse problems: recently, part of my research activity has been devoted to design reliable and effective optimisation tools to tackle sparse BSS problems. This includes investigating dedicated implementations for distributed BSS.