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Deep learning on graphs, also known as Geometric deep learning (GDL) [1], Graph representation learning (GRL), or relational inductive biases, has recently become one of the hottest topics in machine learning. While early works on graph learning go back at least a decade [2], if not two [3], it is undoubtedly the past few years’ progress that has taken these methods from a niche into the spotlight of the Machine Learning (ML) community.

Erik Meijering, the current Chair of SPS’s Bio Imaging and Signal Processing (BISP) Technical Committee, has been pursuing his passion in signal processing from the onset of his education. Realizing early on the vast and evolving ways that signal processing can unlock solutions for the advancement of technology, Erik’s studies began in digital image processing and eventually focused on applications in biomedical imaging for his PhD.

Alan Bovik has a storied career. An American engineer, vision scientist and Primetime Emmy Award winner, Bovik holds the Cockrell Family Endowed Regents Chair in Engineering at The University of Texas at Austin, where he has been the Director of the Laboratory for Image and Video Engineering for more than three decades.

The choice of television programs (and arguably their quality) has exponentially expanded since the first TV was introduced. But the basic concept of watching TV hasn’t changed much. We see video images, whether live or recorded, in two dimensions.