You are cordially invited to the research seminar by Prof. Ercan E Kuruoglu,

Date: Jan 24 2023 (Tuesday)
Time: 14:00
Place: Department of Electrical Engineering, Block D Seminar Room

Title: Robust Adaptive Signal Processing for Time-Varying Graphs

Guest lecturer: Prof Ercan E Kuruoglu, Tsinghua-Berkeley Shenzhen Institute

Abstract

Graphs provide an effective framework for the analysis of multi-variate data. Research in Graph Signal Processing and Graph Neural Networks have advanced to the point of providing state of the art solutions in various applications ranging from traffic data analysis to meteorological data analysis. For the problem of multivariate analysis vertex graph signal processing methods have been developed, in particular graph versions of adaptive signal processing algorithms such as G-LMS have been proposed. We discuss two important remaining challenges of the adaptive graph signal processing problem: non-Gaussian data and time-varying graphs where not only the node values but also the branch weights change over time. We present new algorithms providing time-varying solutions robust to non-Gaussian noise and data.

Biography

Ercan E. Kuruoğlu received MPhil and PhD degrees in information engineering from the University of Cambridge, United Kingdom, in 1995 and 1999, respectively. In 1998, he joined Xerox Research Center Europe, Cambridge. He was an ERCIM fellow in 2000 with INRIA-Sophia Antipolis, France. In January 2002, he joined Institute of Science and Technology of Information-CNR (Italian National Council of Research), Pisa, Italy where he became a Chief Scientist in 2020. He is currently a Full Professor at Tsinghua-Berkeley Shenzhen Institute since 2022. He served as an Associate Editor for the IEEE Transactions on Signal Processing and IEEE Transactions on Image Processing. He was the Editor in Chief of Digital Signal Processing: A Review Journal between 2011-2021. He is currently co-Editor-in-Chief of Journal of the Franklin Institute. He acted as a Technical co-Chair for EUSIPCO 2006 and a Tutorials co-Chair of ICASSP 2014. He is a member of the IEEE Signal Processing Society Data Challenges Committee, IEEE Technical Committees (TC) on Signal Processing Theory and Methods, on Machine Learning for Signal Processing and on Image, Video and Multidimensional Signal Processing and EURASIP Technical Area Committee on Machine Learning. He was a plenary speaker at DAC 2007, ISSPA 2010, IEEE SIU 2017, Entropy 2018, MIIS 2020 and tutorial speaker at IEEE ICSPCC 2012. He was an Alexander von Humboldt Experienced Research Fellow in the Max Planck Institute for Molecular Genetics in 2013-2015. He is listed among the top 2% most cited scientists. His research interests are in the areas of statistical signal and image processing, Bayesian learning and information and coding theory with applications in remote sensing, environmental sciences, telecommunications and computational biology.