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News and Resources for Members of the IEEE Signal Processing Society
Title: Subgraph-Based Networks for Expressive, Efficient, and Domain-Independent Graph Learning
Date: 14 December 2022
Time: 3:00 PM (Paris time) | (Local time zone)
Duration: Approximately 1 hour
Speaker: Dr. Haggai Maron
The DEGAS Webinar Series is an event initiated by the Data Science Initiative (DSI) of the IEEE Signal Processing (SP) Society. The goal is to provide the SP community with updates and advances in learning and inference on graphs. Signal processing and machine learning often deal with data living in regular domains such as space and time. This webinar series will cover the extension of these methods to network data, including topics such as graph filtering, graph sampling, spectral analysis of network data, graph topology identification, geometric deep learning, and so on. Applications can for instance be found in image processing, social networks, epidemics, wireless communications, brain science, recommender systems, and sensor networks. These bi-weekly webinars will be hosted on Zoom, with recordings made available in the IEEE Signal Processing Society’s YouTube channel following the live events. Further details about live and streaming access will follow.
Haggai Maron received the Ph.D. degree in 2019 from the Weizmann Institute of Science under the supervision of Prof. Yaron Lipman.
Currently, he is a Senior Research Scientist at NVIDIA Research and a member of NVIDIA's TLV lab. His main field of interest is machine learning in structured domains. In particular, he works on applying deep learning to sets, graphs, point clouds, and surfaces, usually by leveraging their symmetry structure.
Dr. Haggai will be joining the Faculty of Electrical and Computer Engineering at the Technion as an Assistant Professor in 2023.
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