Adventures in geometric deep learning
In this talk I will present several recent directions of research in the areas of machine learning and applied AI, the so called "geometric deep learning" subfield; common to these topics are the notions of geometry and structure.
I'll start by showing how data-driven methods can serve as efficient and robust drop-in replacements for classical axiomatic constructions, even when learning is not strictly necessary, for typical tasks of geometry and signal processing; in turn, I'll show how geometry itself can serve as a valid proxy for better machine learning pipelines. I'll then explore some preliminary steps toward the application of geometric deep learning in other fundamental disciplines, such as mathematical physics and structural biology, and conclude with an overview of some work in progress in our lab as well as some wishful thinking on future perspectives.
About the speaker
Emanuele Rodolà is Full Professor in Computer Science at Sapienza University of Rome, where he leads the GLADIA group of Geometric Learning & AI funded by an ERC Starting Grant, a Google Research Award, and a PRIN grant.
Previously, he was a postdoc at USI Lugano, TU Munich, and the University of Tokyo. He received a number of awards, including several best paper prizes, has been serving as area chair and in the program committees of the top rated conferences in computer vision and graphics (CVPR, ICCV, ECCV, SGP, EG, etc.), co-founded and chaired the successful workshop on Geometry Meets Deep Learning (co-located with ECCV/ICCV, 2016–2019), gave tutorials and short courses in multiple occasions at EUROGRAPHICS, ECCV, SGP, SIGGRAPH, and was recognized (10 times) as IEEE Outstanding Reviewer at CVPR/ICCV/ECCV. He spent visiting periods at Stanford, Harvard, Ecole polytechnique and Technion among others.
His research interests include geometric deep learning, geometry processing, natural language and sound processing and interactions thereof, and has published around 100 papers on these topics.
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