Davide worked for the formerly ASC Division from May 2022, as a Machine Learning  Engineer. His research study comprised relevant Climate Science-related problems regarding Tropical Cyclones detection and Fire Weather Index prediction through cutting edge Machine Learning approaches.

Now, he is a PhD student in Engineering of Complex Systems in collaboration with the Advanced Digital Innovation Center of CMCC and the University of Salento, in Lecce. The aim of his PhD research study is to design and implement global Machine Learning Foundation Models for the Coupling between the Atmosphere and the Ocean systems, with the ultimate goal of emulating their dynamics in a Subseasonal-to-Seasonal scale.

Davide has both a strong theoretical Machine Learning background in distributed environments and strong Python and programming skills. His main interest is studying and designing data-driven approaches applied to climate change related tasks in order to help research development in this field.

His proficiencies in Machine Learning algorithms comprise many challenging state-of-the-art methodologies and architectures like Transformers, Flow-based Deep Generative models, Mamba, Fourier Neural Operators, UNet, etc.