Antonio Ortega

About

Antonio Ortega is the Dean’s Professor of Electrical and Computer Engineering at the University of Southern California (USC), where he has been a faculty member since 1994. He received his Telecommunications Engineering degree from Universidad Politecnica de Madrid in 1989 and earned his Ph.D. in Electrical Engineering from Columbia University in 1994, supported by a Fulbright scholarship. His research interests encompass signal compression, representation, communication, and analysis, with recent focus areas including distributed compression, multiview coding, error-tolerant compression, information representation in wireless sensor networks, and graph signal processing. Throughout his career at USC, Ortega has served as Associate Chair of EE-Systems and director of the Signal and Image Processing Institute. He is a Fellow of the IEEE, and a member of ACM and APSIPA. His professional service includes roles such as Chair of the Image and Multidimensional Signal Processing technical committee, member of the Board of Governors of the IEEE Signal Processing Society, and chair of the SPS Big Data Special Interest Group. He has held editorial positions with IEEE Transactions on Image Processing, IEEE Signal Processing Magazine, and is the inaugural Editor-in-Chief of the APSIPA Transactions on Signal and Information Processing. Ortega has received numerous awards, including the NSF CAREER award, IEEE Communications Society Leonard G. Abraham Prize Paper Award, and several best…

Research topics

• Computer Science
• Theoretical computer science
• Data science
• Physics
• Mathematics

Selected publications

• Introduction to Graph Signal Processing

  2022 · 108 citations

  1st authorCorresponding

  An intuitive and accessible text explaining the fundamentals and applications of graph signal processing. Requiring only an elementary understanding of linear algebra, it covers both basic and advanced topics, including node domain processing, graph signal frequency, sampling, and graph signal representations, as well as how to choose a graph. Understand the basic insights behind key concepts and learn how graphs can be associated to a range of specific applications across physical, biological a…

  DOI

• Time-Varying Graph Learning with Constraints on Graph Temporal Variation

  arXiv (Cornell University) · 2020 · 32 citations

  Senior authorCorresponding

  We propose a novel framework for learning time-varying graphs from spatiotemporal measurements. Given an appropriate prior on the temporal behavior of signals, our proposed method can estimate time-varying graphs from a small number of available measurements. To achieve this, we introduce two regularization terms in convex optimization problems that constrain sparseness of temporal variations of the time-varying networks. Moreover, a computationally-scalable algorithm is introduced to efficientl…

  DOI

• AutoML for multi-class anomaly compensation of sensor drift

  Measurement · 2025-03-03 · 7 citations

  articleOpen access

  Addressing sensor drift is essential in industrial measurement systems, where precise data output is necessary for maintaining accuracy and reliability in monitoring processes, as it progressively degrades the performance of machine learning models over time. Our findings indicate that the standard cross-validation method used in existing model training overestimates performance by inadequately accounting for drift. This is primarily because typical cross-validation techniques allow data instanc…

  PublisherDOI

• Graph-based Signal Sampling with Adaptive Subspace Reconstruction for Spatially-irregular Sensor Data

  2025-03-12 · 2 citations

  articleSenior author

  Choosing an appropriate frequency definition and norm is critical in graph signal sampling and reconstruction. Most previous works define frequencies based on the spectral properties of the graph and use the same frequency definition and ℓ<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>-norm for optimization for all sampling sets. Our previous work demonstrated that using a sampling-set-dependent norm (and corresponding frequency definition)…

  PublisherDOI

• Joint Optimization of Primary and Secondary Transforms Using Rate-Distortion Optimized Transform Design

  2025-08-18 · 2 citations

  article

  Data-dependent transforms are increasingly being incorporated into next-generation video coding systems such as AVM, a codec under development by the Alliance for Open Media (AOM), and VVC. To circumvent the computational complexities associated with implementing non-separable data-dependent transforms, combinations of separable primary transforms and non-separable secondary transforms have been studied and integrated into video coding standards. These codecs often utilize rate-distortion optimi…

  PublisherDOI

Recent grants

• CIF: Small: Graph Signal Processing Methods for Data-driven System Design

  NSF · $500k · 2020–2024

• CIF: Small: Graph Signal Sampling: Theory and Applications

  NSF · $499k · 2015–2019

• CIF: Small: Wavelets on Graphs - Theory and Applications

  NSF · $500k · 2010–2014

Frequent coauthors

• Ali H. Sayed

  École Polytechnique Fédérale de Lausanne

  5012 shared

• Athina P. Petropulu

  Rutgers, The State University of New Jersey

  4816 shared

• Sergios Theodoridis

  National and Kapodistrian University of Athens

  4816 shared

• Paris Smaragdis

  4814 shared

• Shoji Makino

  Waseda University

  4814 shared

• Béatrice Pesquet‐Popescu

  University of Maryland, Baltimore County

  4758 shared

• Ahmed H. Tewfik

  Apple (United Kingdom)

  4228 shared

• Fernando Pereira

  4096 shared

Education

• PhD, Electrical Engineering

  Columbia University

  1994

Awards & honors

• Fellow of the IEEE
• NSF CAREER award
• 1997 IEEE Communications Society Leonard G. Abraham Prize Pa…
• IEEE Signal Processing Society 1999 Magazine Award
• 2006 EURASIP Journal of Advances in Signal Processing Best P…

Similar researchers at University of Southern California

• Andreas Molischh-132
• Demetrios Christodoulidesh-113
• Bhaskar Krishnamacharih-82