Michael Bronstein
University of Oxford
Twitter

2022-05-17 16:00 ~ 17:00

Online

Zoom Meeting ID: 826-7530-1781

Password: 123456

Conference ID: 858-448-213

Password: 364501

Graph Neural Networks (GNNs) have recently become a standard tool in the machine learning instrumentarium, with applications ranging from social science to particle physics and drug design. Traditionally, GNNs have been built on graph theoretical tools such as the Weisfeiler-Lehman isomorphism tests. In this talk, I will make connections between GNNs and non-Euclidean diffusion equations. I will show that drawing on methods from the domain of differential geometry and algebraic topology, it is possible to describe the expressive power of GNNs, provide a principled view on such architectural choices as positional encoding and graph rewiring, deal with heterophilic datasets, as well as explain and remedy the phenomena of oversmoothing, oversquashing, and bottlenecks.

Michael Bronstein is the DeepMind Professor of AI at the University of Oxford and Head of Graph Learning Research at Twitter. He was previously a professor at Imperial College London and held visiting appointments at Stanford, MIT, and Harvard, and has also 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 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).

Institute of Natural Sciences, Shanghai Jiao Tong University

Shanghai National Center for Applied Mathematics (SJTU Center)

Ministry of Education Key Lab in Scientific and Engineering Computing

AI Biomedicine Center, Zhangjiang IAS, SJTU