Dr. Panagiotis (Panos P.) Markopoulos, Ph.D.

Associate Professor and Margie and Bill Klesse Endowed Professor

Departments of Electrical & Computer Engineering and Computer Science

The University of Texas at San Antonio

About | Publications | GitHub

Multiple research openings available for undergraduate and graduate students.

Areas of Expertise

Dr. Markopoulos is an expert in the areas of machine learning, data science, and signal processing. His research mission is to advance efficient, explainable, and trustworthy artificial intelligence. Dr. Markopoulos focuses on fundamental machine learning (statistical, computational), but also on practical machine-learning solutions to a wide range of real-world problems.


Current research topics:

  • Machine learning with limited, faulty, and corrupted data.

  • Incremental, dynamic, and continual machine learning.

  • Learning from multimodal data and deep learning fusion.

  • Optimizing neural network size and structure, in view of task and available data.

  • Tensor data analysis and processing.

  • Lp-norm formulations for robust machine learning and data analysis.


Among other areas, his research has found important applications in remote sensing, computer vision, communication systems, and healthcare technology.

Biography

Dr. Panagiotis (Panos P.) Markopoulos, Ph.D., is an Associate Professor and Margie and Bill Klesse Endowed Professor with the Departments of Electrical and Computer Engineering and Computer Science at The University of Texas at San Antonio (UTSA). He is also a core faculty member of the UTSA School of Data Science and MATRIX: The UTSA AI Consortium for Human Well-Being. Prior to joining UTSA, Dr. Markopoulos was a tenured Associate Professor with the Rochester Institute of Technology (RIT). In the Summers of 2018, 2020, and 2021, he was a Visiting Research Faculty at the U.S. Air Force Research Laboratory (AFRL), Information Directorate, in Rome NY.

His expertise is in the areas of machine learning, data analysis, and adaptive signal processing. His research mission is to advance efficient, explainable, and trustworthy artificial intelligence. Together with students and collaborators, Dr. Markopoulos has co-authored more than 70 journal and conference articles and 3 book chapters. Since 2016, his research has received external funding awards in the order of $2M (both as PI and Co-PI) from sponsors including the US National Science Foundation (NSF), the US National Geo-Spatial Intelligence Agency, the US Air Force Office of Scientific Research (AFOSR), and the Air Force Research Laboratory (AFRL).

In October 2019, Dr. Markopoulos received the Young Investigator Program (YIP) Award, from the AFOSR. In 2021, Dr. Markopoulos was elevated to the grade of IEEE Senior Member.

Education

Positions

  • Associate Professor and Margie and Bill Klesse Endowed Professor, Aug. 2022 - present.

    • Dept. of Electrical and Computer Engineering and Dept. of Computer Science, The University of Texas at San Antonio

    • Concurrent roles at UTSA:


  • Associate Professor, Aug. 2021 - Aug. 2022.

    • Dept. of Electrical and Microelectronic Engineering, Rochester Institute of Technology, Rochester, NY.

    • Concurrent roles at RIT:

    • Director, Machine Learning Optimization & Signal Processing (MILOS) Lab.

    • Core Faculty Member, RIT Center for Human-aware Artificial Intelligence (CHAI).

    • Extended Faculty Member, Ph.D. Program in Computing and Information Sciences and Ph.D. Program in Mathematical Modeling.

    • Member, RIT Faculty Senate, 2021-2022.


  • Assistant Professor, Aug. 2015 - Aug. 2021.

    • Dept. of Electrical and Microelectronic Engineering, Rochester Institute of Technology, Rochester, NY.


  • Visiting Research Faculty (Summer), 2018, 2019, 2021.

    • U.S. Air Force Research Laboratory (AFRL), Information Directorate, Rome, NY.


  • Graduate Research Assistant, Aug. 2011 - May 2015.

    • SUNY Research Foundation, University at Buffalo (UB), The State University of New York, Buffalo, NY.

Selected Recent Publicatios

All Publications (Google Scholar) | Codes (GitHub)


  • R. Hyder, K. Shao, B. Hou, P. P. Markopoulos, A. Prater-Bennette, and M. S. Asif, "Incremental Task Learning with Incremental Rank Updates," European Conference on Computer Vision (ECCV), 2022. Code

  • M. Dhanaraj and P. P. Markopoulos, "On the Asymptotic L1-PC of Elliptical Distributions," IEEE Signal Processing Letters, 2022.

  • D. G. Chachlakis, M. Dhanaraj, A. Prater-Bennette, P. P. Markopoulos, “Dynamic L1-norm Tucker Tensor Decomposition,” IEEE Journal on Selected Topics in Signal Processing, Special Issue on Tensor Decomposition for Signal Processing and Machine Learning, vol. 15, no. 3, pp. 587-602, April 2021.

  • D. G. Chachlakis and P. P. Markopoulos, “Structured Autocorrelation Matrix Estimation for Coprime Arrays," Signal Processing (Elsevier), vol. 183, no. 107987, June 2021. Code

  • M. Sharma, P. P. Markopoulos, E. Saber, M. S. Asif, and A. Prater-Bennette, "Convolutional Auto-Encoder with Tensor-Train Factorization," in Proc. International Conference on Computer Vision (ICCV 2021, RLS-CV workshop), 2021.

  • M. Sharma, P. P. Markopoulos, and E. Saber, "YOLOrs-LITE: A Lightweight CNN for Real-time Object Detection in Remote Sensing," in Proc. IEEE International Geoscience and Remote Sensing Symposium (IEEE IGARSS), Brussels, Belgium, July 2021.

  • M. Sharma, M. Dhanaraj, D. G. Chachlakis, S. Karam, R. Ptucha, P. P. Markopoulos, E. Saber, “YOLOrs: Object Detection in Multimodal Remote Sensing Imagery,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 1497 - 1508, November 2020.

  • D. G. Chachlakis, P. P. Markopoulos, and A. Prater-Bennette, “L1-Norm Tucker Tensor Decomposition,” IEEE Access, vol. 7, pp. 178454 - 178465, November 2019. Code

  • Y. Liang, P. P. Markopoulos, and E. Saber, “Spatial-Spectral Segmentation of Hyperspectral Images for Subpixel Target Detection,” SPIE Journal of Applied Remote Sensing, vol. 13, no. 3, pp. 036502:1-036502:16, July 2019.

  • P. P. Markopoulos, M. Dhanaraj, and A. Savakis, “Adaptive L1-Norm Principal-Component Analysis with Online Outlier Rejection,” IEEE Journal Selected Topics in Signal Processing, vol. 12, no. 6, pp. 1-13, December 2018.

Recent Funded Projects

  • Title: Target Detection/Tracking and Activity Recognition from Multimodal Data. Funding agency: National Geospatial-Intelligence Agency. Period: September 2019 - September 2024. Total obliged amount: $858,534. Role: Equal effort co-PI (PI: Dr. E. Saber, RIT).

  • AFOSR Young Investigator Program Award. Title: Theory and Efficient Algorithms for Dynamic and Robust L1-Norm Analysis of Tensor Data. Funding agency: U.S. Air Force Office of Scientific Research (AFOSR). Period: January 2020 - January 2023. Amount: $348,460. Role: Sole PI.

  • Title: Collaborative Research: CDS&E: Theoretical Foundations and Algorithms for L1-Norm-Based Reliable Multi-Modal Data Analysis. Funding agency: U.S. National Science Foundation (NSF). Period: September 2018 - August 2021. Amount: $323,973. Role: PI (Co-PI: Dr. A. Savakis, RIT).

  • Title: Efficient Radar Imaging and Machine Learning for Concealed Object Detection. Funding Agency: NYSTAR / UR CoE in Data Science. Period: October 2021 - June 2022. Amount: $58,079. Role: Sole PI.

  • Title: Continual and Incremental Learning with Tensor-Factorized Neural Networks. Funding Agency: U.S. Air Force Research Laboratory (AFRL). Period: September-December 2021. Amount: $30,286. Role: Sole PI.

Selected Awards & Distinctions

  • Young Investigator Program (YIP) Award, Air Force Office of Scientific Research (AFOSR), 2020.

  • Exemplary Performance in Research, Kate Gleason College of Engineering, RIT, 2019, for the research proposals submitted in 2018.

  • Exemplary Performance in Teaching, Kate Gleason College of Engineering (KGCOE), RIT, 2019.

  • Exemplary Performance in Research, Kate Gleason College of Engineering, RIT, 2018, for the research proposals submitted in 2017.

  • Runner-up Poster Award, IEEE Western New York Image and Signal Processing Workshop, 2018, for the paper "Gait recognition based on tensor analysis of acceleration data from wearable sensors."

  • Student Travel Grant Award, SPIE Defense and Commercial Sensing, 2017, for the paper "Adaptive sparse-binary waveform design for all-spectrum channelization."

  • Exemplary Reviewer, IEEE Communications Society, 2017. "For contributions made in furthering the objectives of the Society as Exemplary Reviewer of IEEE Wireless Communications Letters, 2016."

  • Student Travel Grant Award, SPIE Defense, Security, and Sensing, 2014, for the paper "Direction finding with L1-norm subspaces."

  • Best Paper Award in Physical Layer Communications and Signal Processing, IEEE/VTS/EURASIP International Symposium on Wireless Communication Systems, 2013, for the paper "Some options for L1-subspace signal processing."

Societies

  • Senior Member, IEEE (Computer, Signal Processing, Computational Intelligence, and Communication Societies).

  • Member, SIAM.

  • Member, SPIE.

  • Member, American Society for Engineering Education (ASEE).

© Copyright 2022 Panagiotis Markopoulos