Marco graduated from the University of Bologna with a Bachelor’s degree in Physics in 2014, and with a Master’s Degree in Nuclear and Subnuclear Physics in 2018. After high school graduation, he worked a two weeks internship at CERN, Geneva. Under the supervision of Dr Carla Sbarra, Marco learned to analyse calibration data of LHC detectors. During his Master’s Degree, he won an INFN-MAE scholarship to study CMOS pixel detectors at the ATLAS Pixel Laboratory at CERN.
There, in a group supervised by Dr Heinz Pernegger, Marco measured and reconstructed the trajectories of particles hitting a silicon detector. During his master thesis, which took place also at CERN, he further studied the problem of track reconstruction inside a pixel detector, optimising the code of the currently used software. The work of his thesis featured in four scientific publications. After getting his Master’s Degree, he began to study Machine and Deep Learning techniques, to apply them to the work of his master thesis.
In January 2019, he won a scholarship from the Department of Informatics, Science, and Engineering of the University of Bologna to study this particular problem. Besides studying the application of deep learning techniques to particle physics, he also worked on large-scale automated identification of cells in confocal light-sheet microscopy images by using Deep Learning and convolutional neural networks.
Currently, Marco is working on Machine Learning techniques applied to electronic quantum matter imaging experiments.
Supervisors: Professor Barry O’Sullivan