SPS-DSI (DEGAS) Webinar: A Journey Through Graphs for Spatiotemporal Analysis
Date: 9 October 2024
Time: 2:00 PM (Paris Time)
Presenter(s): Jhony H. Giraldo
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. Each webinar speaker will give a lecture, which is followed by Q&A and discussions.
Abstract
Graphs have emerged as powerful tools in spatiotemporal analysis, enabling us to process complex dynamics. This talk embarks on a journey exploring the intersection of graphs and spatiotemporal data. We begin by addressing the challenge of reconstructing time-varying graph signals and presenting practical solutions grounded in graph signal processing and graph neural networks. Next, we will explore the problem of spatiotemporal forecasting, introducing an efficient approach based on causal graph processes. We will conclude with the introduction of continuous graph neural networks in product spaces, which are particularly useful for modeling spatiotemporal data.
Biography
Jhony H. Giraldo received the received the B.Sc. and M.Sc. degrees in electronics engineering from the University of Antioquia, Medellin, Colombia and the Ph.D. in applied mathematics at La Rochelle Université, Laboratoire MIA (Mathématiques, Image, et Applications), France in 2022.
an Assistant Professor at Télécom Paris, Institut Polytechnique de Paris, working in the Information Processing and Communications Laboratory (LTCI) in the Multimedia team. Previously, he held various positions at La Rochelle Université (France), CentraleSupélec - Université Paris-Saclay (France), Università degli Studi di Napoli Parthenope (Italy), the University of Delaware (USA), and Universidad de Antioquia (Colombia).
His research interests include the fundamentals and applications of geometric deep learning (graph and simplicial neural networks), computer vision, machine learning, and graph signal processing.
