Upcoming Webinar: "Deep Learning on Graphs and Manifolds: Going Beyond Euclidean Data"

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News and Resources for Members of the IEEE Signal Processing Society

Upcoming Webinar: "Deep Learning on Graphs and Manifolds: Going Beyond Euclidean Data"

Upcoming Webinar -  20 April 2020
Webinar Topic: "Deep Learning on Graphs and Manifolds:
Going Beyond Euclidean Data"

Presented by Dr. Michael Bronstein

Based on the IEEE Xplore® article: " Geometric Deep Learning: Going Beyond Euclidean Data"
Published in the IEEE Signal Processing Magazine




Dr. Michael Bronstein
20 April 2020
09:00 am EDT (New York time)
Approximately 1 hour
Attendee Registration

Original article will be made freely available for download
on the day of the webinar, on IEEE Xplore®


Register for the Webinar


The IEEE Signal Processing Society would like to express our concern and support for the members of our global community and all affected by the current COVID-19 pandemic. We appreciate your continued patience and support as we work together to navigate these unforeseen and uncertain circumstances. We hope that you, your families, and your communities are safe!

About this Topic:

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 because the non-Euclidean nature of objects dealt with makes the very definition of basic operations used in deep networks rather elusive. This talk will introduce the emerging field of geometric deep learning on graphs and manifolds, provide an overview of existing solutions, and outline the key difficulties and future research directions. As examples of applications, this talk will show problems from the domains of computer vision, graphics, medical imaging, and protein science.

About the Presenter: 

Michael Bronstein

Dr. Michael Bronstein received his Ph.D. degree from the Technion–Israel Institute of Technology, Haifa, in 2007. He has held visiting appointments at Stanford University, CA, Massachusetts Institute of Technology (MIT), Harvard University, MA, and Tel Aviv University, Israel, and has also been affiliated with three Institutes for Advanced Study at Technical University of Munich as Rudolf Diesel Fellow (2017–2019), at Harvard as Radcliffe fellow (2017–2018), and at Princeton (2020).

He is a professor at Imperial College London, where he holds the Chair in Machine Learning and Pattern Recognition, and is 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.

Dr. Bronstein is the recipient of five ERC grants, Fellow of the IEEE and the IAPR, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, he 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 a key contributor to the RealSense 3D sensing technology.

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