Riccardo - Homepage

I am a permanent Research Engineer at the Commissariat à l’énergie atomique et aux énergies alternatives (CEA), Paris-Saclay for the Direction of Technological Research (Direction de la recherche technologique at the Laboratoire de vision pour la modélisation e la localisation, DRT/LIST/DIASI/SIALV/LVML).

My Research

My research interests cover physical and computational problems, the common thread being the relation between applied mathematics and artificial intelligence, from data acquisition to the analysis. At present, I focus on two principal research areas, related to computer vision and data science. The first is the analysis of hyperspectral images (often issued from spectroscopy techniques, such as LIBS or NIR imagery) using supervised and unsupervised methods for object detection and (panoptic) segmentation of scenes. In particular, I am interested in geometric deep learning and representation learning for computer vision: I study the properties of hyperspectral images using graph neural networks and geometry, in order to recover the full extent of the information present in the images. The other is the application of ML and AI to experimental physics. Specifically, I took an interest in explainable AI methods and the definition of uncertainties in deep learning. I actively work in the development of deep learning techniques capable of characterize each measurement, in order to exploit statistical models in order to reject outliers and to provide a measure of the uncertainty of the prediction. I am also interested in applications of machine and deep learning to the theory of mathematics and physics, such as algebraic geometry and string theory, for their fascinating structures and their ability to provide geometrical insights on the behaviour of neural network architectures.

My Thesis

I started my graduate studies in Torino, Italy in 2017, at the Università degli Studi di Torino, under the advisory of Prof. Igor Pesando.

I focused my research on string theory and conformal field theory and applications of deep learning in algebraic geometry. Main topics of my work have been the study of conformal twist and spin fields, cosmological singularities and time dependent orbifolds, and AI techniques for Complete Intersection Calabi-Yau manifolds.

You can find details on my thesis by following the link in the InspireHEP website to the full-text on the I.N.F.N. (National Institute of Nuclear Physics, section of Torino) website: