Davide Buffelli

Ph.D. Student · University of Padova · davide.buffelli [at] phd.unipd.it

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.


News

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!

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Publications


  • Scalable Theory-Driven Regularization of Scene Graph Generation Models
    Davide Buffelli*, Efthymia Tsamoura*, Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023.
    [Paper to appear] [PDF (arXiv)]
  • SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks
    Davide Buffelli, Pietro Liò, Fabio Vandin, Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), 2022.
    [Paper to appear] [PDF (arXiv)] [Code]
  • Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach
    Davide Buffelli, Fabio Vandin, International Joint Conference on Neural Networks (IJCNN), 2022. (Oral)
    [Paper] [PDF (arXiv)] [Code]
  • The Impact of Global Structural Information in Graph Neural Networks Applications
    Davide Buffelli, Fabio Vandin, Data (special issue "Knowledge Extraction from Data Using Machine Learning"), 2022.
    [Paper] [PDF (arXiv)] [Code]
  • Extending Logic Explained Networks to Text Classification
    Rishabh Jain, Gabriele Ciravegna, Pietro Barbiero, Francesco Giannini, Davide Buffelli, Pietro Liò, The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP) [Short Paper], 2022.
    [Paper to appear] [PDF (arXiv)]
  • Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation
    Davide Buffelli, Fabio Vandin, IEEE Sensors Journal, 2021.
    [Paper] [PDF (arXiv)] [Code]
  • A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings
    Davide Buffelli, Fabio Vandin, NeurIPS Workshop on Meta-Learning (MetaLearn), 2020.
    [PDF] [PDF (arXiv; with Appendix)] [Video] [Slides] [Poster] [Code]

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.