I am a Senior Deep Learning Researcher at MediaTek Research in London (UK).
I have a broad interest in Deep Learning, with a current focus on foundation models for multimodal time-series, optimization, and representation learning for structured data.
I hold a Ph.D. in Information Engineering from the University of Padova, where I focused on Graph Neural Networks and Graph Representation Learning and I was supervised by Professor Fabio Vandin.
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.
You check out my full CV here.
11/2024 | Our paper "CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs" has been accepted at LoG 2024! Great collaboration with Farzin Soleymani and Bastian Rieck. |
09/2024 | Two papers accepted at NeurIPS 2024! "Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor Generalization" with colleagues from MediaTek Research, and "Deep Equilibrium Algorithmic Reasoning" in collaboration with Dobrik Georgiev, JJ Wilson, and Pietro Liò . |
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! |
You can send me an email at davide.buffelli [at] mtkresearch.com, and you can find me on Google Scholar, ORCID, LinkedIn, GitHub, and Twitter.