Davide Buffelli

Ph.D. · AI Researcher · davide.buffelli [at] phd.unipd.it

I am a Senior Deep Learning Researcher at MediaTek Research in London (UK). I hold a Ph.D. in Information Engineering from the University of Padova, where I was 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

04/2024 Our paper "The Deep Equilibrium Algorithmic Reasoner" has been accepted at the CVPR Workshop on Multimodal Algorithmic Reasoning! This is a collaboration with the amazing Dobrik Georgiev and Pietro Liò on a new model for Neural Algorithmic Reasoning.
05/2023 I am starting a new position as a Senior Deep Learning Researcher at MediaTek Research! I look forward to working with this amazing team.
03/2023 I have successfuly defended my PhD thesis (available here)! I look forward to the next steps in my career.
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.

View Older News

Publications


  • The Deep Equilibrium Algorithmic Reasoner
    Dobrik Georgiev, Pietro Liò, Davide Buffelli, CVPR Workshop on Multimodal Algorithmic Reasoning, 2024.
    [PDF (arXiv)]
  • Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?
    Davide Buffelli, Ashish Gupta, Agnieszka Strzalka, Vassilis Plachouras, Preprint, 2023.
    [PDF (arXiv)]
  • Improving the Effectiveness of Graph Neural Networks in Practical Scenarios
    Davide Buffelli, PhD Thesis, University of Padova, 2023.
    [Full Text]
  • Scalable Theory-Driven Regularization of Scene Graph Generation Models
    Davide Buffelli*, Efthymia Tsamoura*, Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023.
    [Paper] [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] [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), 2022.
    [Paper] [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.