Introduction to Aspects of Graph Signal Processing: Tensor Valued Signal Reconstruction 🗓

Sponsor: Central Coast Section
Speaker: Prof. Gareth Peters of UCSB
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Meeting Date: 20 Nov 2024
Time: 06:00 PM to 08:30 PM
Cost:
Location: Rusty’s Pizza
Reservations: IEEE
Summary:
In this presentation a review of core ideas on graph signal reconstruction and regression are covered in single graph and graph product tensor valued data settings. The talk will largely be a basic introduction to aspects of the methodology in this emerging domain. The talk is based on published work:

Edward Antonian, Gareth W. Peters, Michael Chantler, Bayesian reconstruction of Cartesian product graph signals with general patterns of missing data, Journal of the Franklin Institute, Volume 361, Issue 9, 2024, https://doi.org/10.1016/j.jfranklin.2024.106805.

Bio: Prof. Gareth Peters of UCSB ris the first Endowed Chair Prof. of the Department of Statistics and Applied Probability in the University of California Santa Barbara. He holds the Duncan Chair of Actuarial Science and is a Professor of Statsitics for Risk and Insurance. He is also the director of the newly established Future AI Research for Actuarial Intelligence fairAI-Institute. Previously Prof. Peters was the Chair Professor of Actuarial Science in Heriot-Watt University Scotland and the Director of the Scottish Financial Risk Academy. He has also held tenured positions in University College London and the University of New South Wales, Sydney Australia. Prof. Peters has published 5 research textbooks on risk and insurance, spatial-temporal processes and Monte Carlo methods. He has published over 130 journal papers and over 60 conference papers. He holds a BSc (Mathematics and Physics – hons 1st) and BEng (Electrical, Communications and Signal Processing) from Melbourne University, a MSc (Research – Statistical Signal Processing) University of Cambridge and PhD (Mathematics and Statsitics) University of New South Wales. For further details of Prof. Peters work or to get in touch see his research webpage: https://www.qrslab.com/

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