About Me
I am a Ph.D. student at the Graph Machine Learning Group, within the Swiss AI lab IDSIA and USI - Università della Svizzera italiana (Switzerland), under the supervision of Prof. Cesare Alippi. Additionally, I am enrolled in the ELLIS Ph.D. program under the joint supervision of Prof. Cesare Alippi and Prof. Michael Bronstein.
I am currently on a research visit at the University of Oxford, collaborating with Prof. Michael Bronstein.
Previously, I obtained BSc (2017) and MSc (2020) degrees in Computer Science and Engineering at Politecnico di Milano (IT). My master thesis project has been supervised by Prof. Nicola Gatti.
What I'm Doing
My research focuses on graph deep learning for irregular spatiotemporal data. I study the problems of imputation, regularization, and prediction of data coming over time from both physical and virtual sensor networks.
You can find a list of my publications here.
Spatiotemporal data
My studies focus on data spanning the temporal and spatial dimensions.
Irregular data
Temporal and spatial irregularities pose an intriguing challenge.
Graph Neural Networks
My focus is on graph deep learning – GNNs in particular.
Real-world applications
I love to see how my research can be game-changer in practical applications.
Open Source Projects
I strongly believe in the 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
Torch Spatiotemporal (TSL) is a library built upon PyTorch and PyG for neural spatiotemporal data processing, with a focus on Graph Neural Networks.