2022 IEEE International Conference on Acoustics, Speech and Signal Processing

7-13 May 2022
  • Virtual (all paper presentations)
22-27 May 2022
  • Main Venue: Marina Bay Sands Expo & Convention Center, Singapore
  • Satellite Venue: Shenzhen, China (Postponed)

EXP-9: Convolutional Neural Networks on Graphs
Fri, 27 May, 08:30 - 09:30 Singapore Time (UTC +8)
Fri, 27 May, 02:30 - 03:30 France Time (UTC +2)
Fri, 27 May, 00:30 - 01:30 UTC
Thu, 26 May, 20:30 - 21:30 New York Time (UTC -4)
Location: TBD
Xavier Bresson, National University of Singapore

In the past years, 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 grid-structured data, while many important applications have to deal with graph structured data. Such geometric data are becoming increasingly important in computer graphics and 3D vision, sensor networks, drug design, biomedicine, recommendation systems, NLP and computer vision with knowledge graphs, and web applications. The purpose of this talk is to introduce convolutional neural networks on graphs, as well as applications of these new learning techniques.

Speaker Biography

Xavier Bresson is an Associate Professor in the Department of Computer Science at the National University of Singapore (NUS). His research focuses on Graph Deep Learning, a new framework that combines graph theory and neural network techniques to tackle complex data domains. In 2016, he received the US$2.5M NRF Fellowship, the largest individual grant in Singapore, to develop this new framework. He was also awarded several research grants in the U.S. and Hong Kong. He co-authored one of the most cited works in this field (10th most cited paper at NeurIPS), and he has recently introduced with Yoshua Bengio a benchmark that evaluates graph neural network architectures. He has organized several workshops and tutorials on graph deep learning such as the recent IPAM'21 workshop on "Deep Learning and Combinatorial Optimization", the MLSys'21 workshop on "Graph Neural Networks and Systems", the IPAM'19 and IPAM'18 workshops on "New Deep Learning Techniques", and the NeurIPS'17, CVPR'17 and SIAM'18 tutorials on "Geometric Deep Learning on Graphs and Manifolds". He has been a regular invited speaker at universities and companies to share his work. He has also been a speaker at the KDD'21, AAAI'21 and ICML'20 workshops on "Graph Representation Learning", and the ICLR'20 workshop on "Deep Neural Models and Differential Equations". He has taught graduate courses on Deep Learning and Graph Neural Networks at NUS, and as a guest lecturer for Yann LeCun's course at NYU. Twitter:, Scholar:, GitHub:, LinkedIn: