Prof. Antonio Ortega (USC) visits WISENET

1. September 2017
|In Ukategorisert
|By Baltasar Beferull Lozano

Antonio Ortega, Professor, University of Southern California, visited the WISENET Lab from August 15th to August 18th. Prof. Ortega gave a very interesting Talk “Learning Graphs from Data” and attended several presentations given by PhD students in our Lab related to Graph Signal Processing, providing useful feedback.

 

GUEST LECTURE,
WEDNESDAY 16 TH OF AUGUST 2017
TIME: 10:30 11:45,
ROOM C2 041
PROFESSOR ANTONIO ORTEGA
UNIVERSITY OF SOUTHERN CALIFORNIA
LEARNING GRAPHS FROM DATA

Abstract
There has been significant recent progress in the development of tools
for graph signal processing, including methods for sampling and transforming
graph signals In many applications, a graph needs to be learned from data
before these graph signal processing methods can be applied A standard
approach for graph learning is to estimate the empirical covariance from the data
and then compute an inverse covariance ( matrix under desirable
structural constraints We present recent results that allow us to solve these
problems under constraints that encompass a broad class of generalized graph
Laplacians These methods are computationally efficient, can incorporate
sparsity constraints, and can also be used to optimize weights for a given known
topology We illustrate these ideas with examples in image processing and other
areas
Bio
Antonio Ortega received the Telecommunications Engineering degree from
the Universidad Politecnica de Madrid, Madrid, Spain in 1989 and the Ph D in
Electrical Engineering from Columbia University, New York, NY in 1994 In 1994
he joined the Electrical Engineering department at the University of Southern
California ( where he is currently a Professor and has served as Associate
Chair He is a Fellow of the IEEE 2007 and a member of ACM and APSIPA He is
currently a member of the Board of Governors of the IEEE Signal Processing
Society and the inaugural Editor in Chief of the APSIPA Transactions on Signal
and Information Processing He has received several paper awards, including
most recently the 2016 Signal Processing Magazine award and was a plenary
speaker at ICIP 2013 His recent research work is focusing on graph signal
processing, machine learning, multimedia compression and wireless sensor
networks About 40 PhD students have completed their PhD thesis under his
supervision at USC and his work has led to over 300 publications in international
conferences and journals, as well as several patents.