I'm a Ph.D. candidate in Information Engineering at the University of Padova (expected graduation: March 2023), supervised by Professor Fabio Vandin.
I have a broad interest in Deep Learning, with a current focus on techniques for structured data (geometric Deep Learning), and in particular Graph Neural Networks and Graph Representation Learning.
I hold a Bachelor's degree in Information Engineering and a Master's degree in Computer Engineering, both from the University of Padova. You check out my CV here.
During my PhD I have been a Research Scientist Intern at Meta AI (London) and at Samsung AI Research (Cambridge), and a visiting student in Professor Pietro Liò's group at the University of Cambridge, and in Dr. Bastian Rieck's group at Helmholtz Munich.
|11/2022||Our paper "Scalable Theory-Driven Regularization of Scene Graph Generation Models" has been accepted for publication at AAAI 2023!|
|09/2022||Our paper "SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks" has been accepted for publication at NeurIPS 2022!|
|09/2022||I am starting as a Research Scientist Intern at Meta AI in London!|
|08/2022||I have been awarded a Helmholtz Visiting Researcher Grant by HIDA. I will then visit Dr. Bastian Rieck at Helmholtz Munich to collaborate on exciting projects at the intersection of GNNs and topology!|
|07/2022||I have given a talk at the University of Cambridge about Graph Neural Networks and the problem of size-generalization. Check out the recording here.|
|04/2022||Our paper "Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach" has been accepted as oral at IJCNN 2022!|
You can send me an email at davide.buffelli [at] phd.unipd.it, and you can find me on Google Scholar, ORCID, LinkedIn, GitHub, and Twitter.