Emanuele obtained his B.Sc. in Information Technology Engineering on 29July 2019 at the University of Salento, with a thesis in Computer Networks entitled “Design and development of the back-end sub-system for an event management service”.

He continued his studies at the University of Salento and on 22 February 2023, he obtained the M.Sc. in Computer Engineering with a thesis in High Performance Computing entitled “Global downscaling of remotely sensed soil moisture retrievals using Machine Learning”.

In this thesis work, he proposed a new approach capable of downscale soil moisture from a spatial resolution of 3 km to a spatial resolution under 1 km (~750m) using a Machine Learning approach based on Artificial Neural Network architectures, in particular Feed Forward Neural Networks. His work is one of the few that uses a machine learning approach and neural networks to downscale soil moisture, focusing on the global domain.

Since June 2023, he has been working as a Post-Degree in Machine Learning branch of the Advanced Scientific Computing (ASC) division at the Euro-Mediterranean Center on Climate Change (CMCC).

He has both a good theoretical Machine Learning background and Python programming skills. His main interest is studying and developing strong Machine Learning skills applied to climate change related tasks in order to help research development in this field.