Time-varying Signal Estimation Using Dynamic Topological Graphs

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• Prof. Ercan E Kuruoglu, Tsinghua University Shenzhen International Graduate School
• Thursday 06 February 2025, 15:00-16:00
• JDB Seminar Room, CUED.

If you have a question about this talk, please contact Prof. Ramji Venkataramanan.

The common assumption of the static graph with signals only residing on the nodes of a graph is an oversimplification for representing multivariate time-varying signals interacting over time. Various real life signal networks such as brain connectivity networks, gene-expression networks, meteorological networks change over time, in addition to node signals changing over time, the relationships between signals represented with edge attributes and even the topology of networks change over time. In this talk, we will present adaptive graph signal processing methods for the solution of problems starting from the estimation of time-varying signals on the nodes to the estimation of signals simultaneously time-varying on the nodes and the edges. We will present the spectral topological graph analysis framework which will make our algorithms seamless and end with a new method for following changes in graph topologies. We will present our results on various applications including mobility data modelling, brain connectivity and gene expression networks.

Bio: Ercan E. Kuruoğlu received MPhil and PhD degrees in information engineering from the University of Cambridge, United Kingdom, in 1995 and 1998, 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 ISTI -CNR, Pisa, Italy where he became a Chief Scientist in 2020. Currently, he is a Full Professor at Tsinghua-Berkeley Shenzhen Institute since March 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 Technical Committees (TC) on Machine Learning for Signal Processing and on Image, Video and Multidimensional Signal Processing. He is also a member of the IEEE Data Collections and Challenges Committee. He was a plenary speaker at ISSPA 2010 , IEEE SIU 2017 , Entropy 2018, MIIS 2020 , IET IRC 2023 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 2012-2014. His research interests are in the areas of statistical signal and image processing, Bayesian machine learning and information theory with applications in remote sensing, environmental sciences, telecommunications and computational biology.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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