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SUMMARY:Time-varying Signal Estimation Using Dynamic Topological Graphs - 
 Prof. Ercan E Kuruoglu\, Tsinghua University Shenzhen International Gradua
 te School
DTSTART:20250206T150000Z
DTEND:20250206T160000Z
UID:TALK228262@talks.cam.ac.uk
CONTACT:Prof. Ramji Venkataramanan
DESCRIPTION:The common assumption of the static graph with signals only re
 siding on the nodes of a graph is an oversimplification for representing m
 ultivariate time-varying signals interacting over time. Various real life 
 signal networks such as brain connectivity networks\, gene-expression netw
 orks\, meteorological networks change over time\, in addition to node sign
 als changing over time\, the relationships between signals represented wit
 h edge attributes and even the topology of networks change over time. In t
 his talk\, we will present adaptive graph signal processing methods for th
 e solution of problems starting from the estimation of time-varying signal
 s on the nodes to the estimation of signals simultaneously time-varying on
  the nodes and the edges. We will present the spectral topological graph a
 nalysis framework which will make our algorithms seamless and end with a n
 ew method for following changes in graph topologies. We will present our r
 esults on various applications including mobility data modelling\, brain c
 onnectivity and gene expression networks.\n\n*Bio*: Ercan E. Kuruoğlu rec
 eived 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 fell
 ow in 2000 with INRIA-Sophia Antipolis\, France. In January 2002\, he join
 ed ISTI-CNR\, Pisa\, Italy where he became a Chief Scientist in 2020. Curr
 ently\, he is a Full Professor at Tsinghua-Berkeley Shenzhen Institute sin
 ce 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 20
 11-2021. He is currently co-Editor-in-Chief of Journal of the Franklin Ins
 titute. 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 Mul
 tidimensional Signal Processing. He is also a member of the IEEE Data Coll
 ections and Challenges Committee. He was a plenary speaker at ISSPA 2010\,
  IEEE SIU 2017\, Entropy 2018\, MIIS 2020\, IET IRC 2023 and tutorial spea
 ker at IEEE ICSPCC 2012. He was an Alexander von Humboldt Experienced Rese
 arch Fellow in the Max Planck Institute for Molecular Genetics in 2012-201
 4. His research interests are in the areas of statistical signal and image
  processing\, Bayesian machine learning and information theory with applic
 ations in remote sensing\, environmental sciences\, telecommunications and
  computational biology.
LOCATION:JDB Seminar Room\, CUED
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