Posts

Reacently I had the pleasure of talking about graphs and fairness at “Universit√† della Svizzera Italiana”. The talk was hosted by the Graph Machine Learning Group of Prof. Cesare Alippi and organized by Daniele Grattarola. For a buch of slides look Here.

CONTINUE READING

Here you can find an introduction to the world of graph neural networks (GNN for short). This family of models is able process graph structured data and solve many interesting tasks.

CONTINUE READING

We recently released a teaser of PLVS in action. Here you can find more info and a lot of videos of PLVS showing its capabilities on many different datasets. PLVS is still a work in progress but the software will be released as open-source with our outcoming paper very soon!

CONTINUE READING

This is the first post of my personal page and in some sense, the story the beginning of my interest in research. I had the privilege to collaborate as a bachelor and later master student to an European project TRADR: Long-Term Human-Robot Teaming for Disaster Response. It has not been always easy to make this intern at the Alcor lab coexist with my ordinary studies, but was worth it. I’ve learned a lot of stuff, travelled as much and made new connections.

CONTINUE READING

Publications

. A Meta-Learning Approach for Training Explainable Graph Neural Networks. 2021.

PDF Code

. FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning. 2021.

PDF Code Slides

. Distribuited Graph Convolutional Network. 2020.

PDF

. Adaptive Propagation Graph Convolutional Network. IEEE TNNLS, 2020.

PDF Code

. Missing Data Imputation with Adversarially-trained Graph Convolutional Networks. Neural Networks, 2020.

PDF Code

. Efficient data augmentation using graph imputation neural networks. WIRN, 2019.

PDF

Teaching

20192020

Teaching Assistant

Contact

You can reach me at …