Wednesday 22nd January 2020
Ground Floor Seminar Room
25 Howland Street, London, W1T 4JG
Deep learning on graphs and manifolds: going beyond Euclidean data
In the past decade, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. So far research has mainly focused on developing deep learning methods for Euclidean-structured data. However, many important applications have to deal with non-Euclidean structured data, such as graphs and manifolds. Such data are becoming increasingly important in computer graphics and 3D vision, sensor networks, drug design, biomedicine, high energy physics, recommendation systems, and social media analysis. The adoption of deep learning in these fields has been lagging behind until recently, primarily since the non-Euclidean nature of objects dealt with makes the very definition of basic operations used in deep networks rather elusive. In this talk, I will introduce the emerging field of geometric deep learning on graphs and manifolds, overview existing solutions and outline the key difficulties and future research directions. As examples of applications, I will show problems from the domains of computer vision, graphics, medical imaging, and protein science.
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. His main research expertise is in theoretical and computational methods for geometric data analysis, a field in which he has published extensively in the leading journals and conferences. He is credited as one of the pioneers of geometric deep learning, generalizing machine learning methods to graph-structured data. Michael received his PhD from the Technion (Israel Institute of Technology) 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 Rudolf Diesel Fellow (2017-), at Harvard as Radcliffe fellow (2017-2018), and at Princeton (2020)). Michael is the recipient of five ERC grants, Fellow of IEEE and IAPR, 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.