Michael Bronstein
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. 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, five ERC grants, two Google Faculty Research Awards, and two Amazon AWS ML 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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Fisher Flow Matching for Generative Modeling over Discrete Data
Oscar Davis‚ Samuel Kessler‚ Mircea Petrache‚ İsmail İlkan Ceylan‚ Michael Bronstein and Avishek Joey Bose
In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS). 2024.
Details about Fisher Flow Matching for Generative Modeling over Discrete Data | BibTeX data for Fisher Flow Matching for Generative Modeling over Discrete Data | Link to Fisher Flow Matching for Generative Modeling over Discrete Data
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Learning on Large Graphs using Intersecting Communities
Ben Finkelshtein‚ İsmail İlkan Ceylan‚ Michael Bronstein and Ron Levie
In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS). 2024.
Details about Learning on Large Graphs using Intersecting Communities | BibTeX data for Learning on Large Graphs using Intersecting Communities | Link to Learning on Large Graphs using Intersecting Communities
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Homomorphism Counts for Graph Neural Networks: All About That Basis
Emily Jin‚ Michael Bronstein‚ İsmail İlkan Ceylan and Matthias Lanzinger
In Proceedings of Fourty−first International Conference on Machine Learning (ICML). 2024.
Details about Homomorphism Counts for Graph Neural Networks: All About That Basis | BibTeX data for Homomorphism Counts for Graph Neural Networks: All About That Basis | Link to Homomorphism Counts for Graph Neural Networks: All About That Basis