I am a Ph.D. student at the Graph Machine Learning Group, within the Swiss AI lab IDSIA, at Università della Svizzera italiana (USI), under the supervision of Prof. Cesare Alippi. My research focuses on problems regarding irregular spatiotemporal data, like prediction, imputation, and control on sensor networks using geometric deep learning.
I obtained BSc (2017) and MSc (2020) degrees in Computer Science and Engineering at Politecnico di Milano. My master thesis project has been supervised by Prof. Nicola Gatti.
Currently, I am a Ph.D. Student at the Swiss AI Lab IDSIA at USI Università della Svizzera Italiana, under the supervision of prof. Cesare Alippi.
Master’s degree with honors (110/110L), defending a thesis on machine learning. During the two years of studies, I mostly attended AI-oriented courses.
During the semester spent abroad (in Valencia) within the Erasmus program, I attended Spanish and English courses on programming, robotics and artificial intelligence.
The course program covered general topics of engineering and computer science.
High school diploma with a specific focus in mathematics and science at Liceo Scientifico Caminiti in Santa Teresa di Riva (Sicily).
Awards & Scholarships
I believe in worldwide accessibility of science. As such, I make the software I develop for my research publicly available (whenever possible) through my GitHub page. You can also find the code related to my publications on the GitHub page of Graph Machine Learning Group.
Torch Spatiotemporal (TSL) is a library built upon PyTorch and PyG for neural spatiotemporal data processing, with a focus on Graph Neural Networks.GitHub Documentation