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DTC Seminar Series
Signal and Image Processing Institute
Department of Electrical Engineering
University of Southern California
Los Angeles, CA
Thursday, March 30, 2017
4:30 p.m. Reception with light refreshments
5:00 p.m. Seminar
401/402 Walter Library
Graphs have long been used in a wide variety of problems, such analysis of social networks, machine learning, network protocol optimization, decoding of LDPCs or image processing. Techniques based on spectral graph theory provide a "frequency" interpretation of graph data and have proven to be quite popular in multiple applications. In the last few years, a growing amount of work has started extending and complementing spectral graph techniques, leading to the emergence of "Graph Signal Processing" as a broad research field. A common characteristic of this recent work is that it considers the data attached to the vertices as a "graph-signal" and seeks to create new techniques (filtering, sampling, interpolation), similar to those commonly used in conventional signal processing (for audio, images or video), so that they can be applied to these graph signals. In this talk, we first introduce some of the basic tools needed in developing new graph signal processing operations, with a brief overview of our design of wavelet filterbanks of graphs. We then present our recent work on sampling of graph signals and graph topology learning. We illustrate the basic concepts with example applications in the areas of semi-supervised learning and video coding.
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 (USC), where he is currently a Professor and where he has served as Associate Chair. He is also a visiting Professor at National Institute of Informatics, Tokyo, Japan. He is a Fellow of the IEEE and a member of ACM and APSIPA. He is currently a member of the Board of Governors of the IEEE Signal Processing Society (SPS), the inaugural Editor-in-Chief of the APSIPA Transactions on Signal and Information Processing, launched by APSIPA and Cambridge University Press in 2012, and a senior area editor for IEEE Transactions on Image Processing. He has received several paper awards, including most recently the 2016 IEEE Signal Processing Magazine Award. His recent research work has focused on multiview coding, error tolerant compression, wavelet-based signal analysis, wireless sensor networks and graph signal processing. Close to 40 PhD students have completed their PhD thesis under his supervision at USC and his work has led to about 400 publications in international conferences and journals, as well as several patents.