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Webinar Speaker: Dr James Yu
Title: Deep learning on graphs with applications in smart cities research
Date & Time: Wed, Jul 3, 2019 1:00 PM - 2:00 PM GMT
To Register Click here: https://attendee.gotowebinar.com/register/3340088026348409602
Deep learning is successful in many research and engineering domains, ranging from acoustics, images to natural language processing. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges to apply deep learning to the ubiquitous data structure. Recently, a significant amount of research efforts have been devoted to this area, greatly advancing graph analyzing techniques. In this talk, I will provide an introductory overview of graph neural networks in data mining and machine learning fields, with a focus on graph convolutional networks. I will review alternative architectures that have recently been developed, and discuss the applications of graph neural networks on classical network-related tasks and recent applications in smart cities research.
James is an assistant professor at Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), China, and an honorary assistant professor at Department of Electrical and Electronic Engineering, the University of Hong Kong. He is also the chief research consultant of GWGrid Inc. (Zhuhai) and Fano Labs (Hong Kong). He received the B.Eng. and Ph.D. degree from the University of Hong Kong in 2011 and 2015, respectively. Before joining SUSTech, he was a post-doctoral fellow at the University of Hong Kong. He is broadly interested in smart city and urban computing, deep learning, intelligent transportation systems, and smart energy systems. He is an Editor of the IET Smart Cities journal and the Leading Editor of its special issue on Smart Transport.
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