Max Welling

Graph Neural Networks for Knowledge Representation and Recommendation

by Max Welling

Graph Neural Networks have gained enormous popularity in recent years and found widespread application in, among others, physics and chemistry, computer vision, simulation, healthcare, wireless communication, logistics, natural language processing, causality, knowledge representation and recommendation. In this talk I will give a brief introduction on graph neural networks and their relation to deep learning. I will also discuss how to incorporate symmetries in GNNs and discuss a number of applications on which I have worked. In the last part of the talk I will discuss in a little bit more detail how GNNs can be applied to KR, IR and recommender systems.

About the speaker

Max Welling is a research chair in Machine Learning at the University of Amsterdam and a Distinguished Scientist at MSR. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he also serves on the founding board. His previous appointments include VP at Qualcomm Technologies, professor at UC Irvine, postdoc at U. Toronto and UCL under supervision of Prof. Geoffrey Hinton, and postdoc at Caltech under supervision of prof. Pietro Perona. He finished his PhD in theoretical high energy physics under supervision of Nobel laureate Prof. Gerard ‘t Hooft.

Max Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015, he serves on the advisory board of the NeurIPS foundation since 2015 and has been program chair and general chair of NeurIPS in 2013 and 2014 respectively. He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair of MIDL 2018. Max Welling is recipient of the ECCV Koenderink Prize in 2010 and the ICML Test of Time award in 2021. He directs the Amsterdam Machine Learning Lab (AMLAB) and co-directs the Qualcomm-UvA deep learning lab (QUVA) and the Bosch-UvA Deep Learning lab (DELTA).


Monday, Sept 27, 14:00-14:45

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