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Deep Learning on Graphs


12 November 2020

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Deep Learning on Graphs: Successes, Challenges, and Next Steps


Deep learning on graphs and network-structured data has recently become one of the hottest topics in machine learning. Graphs are powerful mathematical abstractions that can describe complex systems of relations and interactions in fields ranging from biology and high-energy physics to social science and economics. In this talk, I will outline the basic methods, applications, challenges and possible future directions in the field.

About the Speaker

Michael Bronstein is a professor at Imperial College London, where he holds the Chair in Machine Learning and Pattern Recognition, and Head of Graph Learning Research at Twitter. Michael received his PhD from the Technion in 2007. He has held visiting appointments at Stanford, MIT, Harvard, and Tel Aviv University, and has also been affiliated with three Institutes for Advanced Study (at TU Munich as a Rudolf Diesel Fellow (2017-), at Harvard as a Radcliffe fellow (2017-2018), and at Princeton (2020)). Michael is the recipient of five ERC grants, Fellow of IEEE, IAPR, 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). He has previously served as Principal Engineer at Intel Perceptual Computing and was one of the key developers of the Intel RealSense technology.

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