Michael Bronstein
Professor Michael Bronstein
Wolfson Building, Parks Road, Oxford OX1 3QD
Interests
Geometric deep learning, graph neural networks, 3D shape analysis, protein design, non-human species communication
Biography
Michael Bronstein is the DeepMind Professor of AI at the University of Oxford and Founding Scientific Director, AI at the Aithyra Institute in Vienna. He was previously Head of Graph Learning Research at Twitter, a professor at Imperial College London and held visiting appointments at Stanford, MIT, and Harvard. He has been affiliated with three Institutes for Advanced Study (at TUM as a Rudolf Diesel Fellow (2017-2019), at Harvard as a Radcliffe fellow (2017-2018), and at Princeton as a short-time scholar (2020)). Michael received his PhD from the Technion in 2007. He is the recipient of the EPSRC Turing AI World Leading Research Fellowship, Royal Society Wolfson Research Merit Award, Royal Academy of Engineering Silver Medal, as well as multiple ERC grants, Google Faculty Research Awards, and Amazon Research Awards. He is a Member of the Academia Europaea, Fellow of IEEE, IAPR, BCS, and ELLIS, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019).
Selected Publications
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Link prediction with relational hypergraphs
Xingyue Huang‚ Miguel Romero Orth‚ Pablo Barceló‚ Michael M Bronstein and İsmail İlkan Ceylan
In TMLR. 2025.
Details about Link prediction with relational hypergraphs | BibTeX data for Link prediction with relational hypergraphs
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Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models
Ben Finkelshtein‚ İsmail İlkan Ceylan‚ Michael Bronstein and Ron Levie
In Proceedings of the 39th Annual Conference on Neural Information Processing Systems (NeurIPS). 2025.
Details about Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models | BibTeX data for Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models
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Curly Flow Matching for Learning Non−gradient Field Dynamics
Katarina Petrović‚ Lazar Atanackovic‚ Viggo Moro‚ Kacper Kapuśniak‚ İsmail İlkan Ceylan‚ Michael Bronstein‚ Michael Bronstein‚ Avishek Joey Bose and Alexander Tong
In Proceedings of the 39th Annual Conference on Neural Information Processing Systems (NeurIPS). 2025.
Details about Curly Flow Matching for Learning Non−gradient Field Dynamics | BibTeX data for Curly Flow Matching for Learning Non−gradient Field Dynamics