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Associate Professor & Cloud Technology Endowed Fellow
Departments of Computer Engineering and Computer Science
College of AI, Cyber and Computing
The University of Texas at San Antonio

Contact

• Mailing Address: The University of Texas at San Antonio, San Pedro I, 506 Dolorosa St., San Antonio, TX 78204

• E-mail: panos@utsa.edu; panagiotis.markopoulos.phd@gmail.com

• Phone: 210-458-6482

• Links: College Profile; LinkedIn; ORCID; Google Scholar

Brief Biography

Dr. Panagiotis (Panos) Markopoulos is an Associate Professor and Cloud Technology Endowed Fellow in the Departments of Computer Engineering and Computer Science at The University of Texas at San Antonio. He is the Founding Director of the Machine Learning Optimization and Systems (MILOS) Laboratory and serves as Thrust Lead for Trustworthy AI in The UTSA AI Consortium for Human Well-Being (MATRIX). His research interests include machine learning, signal processing, computer vision, remote sensing, wireless communications, quantum computing, numerical/stochastic optimization, and multilinear algebra, all with emphasis on solutions for high-stakes and resource-constrained applications in defense, healthcare, cybersecurity, and networked autonomous systems. He has authored more than 95 peer-reviewed journal and conference papers and has received research funding from the National Science Foundation, the Air Force Office of Scientific Research, the National Geospatial-Intelligence Agency, and other agencies. Dr. Markopoulos is a recipient of the AFOSR Young Investigator Program Award. He is an IEEE Senior Member and a member of SPIE and ASEE. He has been elected a member of IEEE-Eta Kappa Nu (IEEE-HKN), the Honor Society of IEEE, and Sigma Xi, The Scientific Research Honor Society. He received the Ph.D. degree in electrical engineering from the University at Buffalo in 2015 and the Diploma and M.Sc. degrees, both in Electronic and Computer Engineering, from the Technical University of Crete, Greece, in 2010 and 2012, respectively.

Research Areas

Technical Domains

• Machine Learning (statistical learning and deep learning). Topics include: Robust and trustworthy learning from challenging data (e.g., limited, high-dimensional, corrupted, imbalanced); continual learning (adaptive learning from streaming data in evolving environments, mitigating catastrophic forgetting); federated learning (privacy-preserving learning from distributed datasets); computationally efficient learning and inference for edge-AI deployments.

• Signal Processing. Topics include: Statistical and adaptive signal processing; stochastic and numerical optimization; sensor array processing; robust subspace estimation and adaptation (e.g., Lp-PCA methods); multi-linear algebra and tensor methods (e.g., robust L1-norm Tucker/PARAFAC decompositions and dynamic tensor factorization for compact, compressed, and adaptive neural networks).

• Wireless Communications and Sensing. Topics include: Radar and localization; physical-layer wireless communications (smart antennas, adaptive filtering, beamforming, spread-spectrum waveform design, channel estimation and equalization, interference suppression, symbol modulation/detection/estimation, resource allocation, multiplexing, multi-user MIMO).

• Computer Vision and Remote Sensing. Topics include: image denoising, segmentation, classification; multi-scale and rotation-invariant object detection and tracking; fusion of multi-modal imagery (e.g., hyperspectral, EO, SAR).

• Quantum Computing. Topics include: hybrid quantum-classical methods for machine learning; adaptive task distribution between quantum and classical resources; quantum annealing for combinatorial optimization in machine learning and signal processing.

Selected Application Domains

• Defense and National Security: Radar and RF systems; remote sensing and geospatial intelligence; computer vision for surveillance and target detection and tracking; robust machine learning for mission-critical decision-support.

• Autonomous and Networked AI Systems: Perception, connectivity, and navigation for autonomous platforms; multi-modal sensor fusion; adaptive and distributed learning over edge-AI deployments; secured autonomy.

• Healthcare and Biomedical Sensing: machine learning for medical and behavioral-health data; wearable and gait sensing; radar- and sensor-based human activity recognition; privacy-preserving and federated learning in healthcare and clinical research environments.

Education

• Ph.D., Electrical Engineering, 2015
  University at Buffalo (UB), The State University of New York, Buffalo, NY, USA
  Dissertation title: “Optimal Algorithms for L1-norm Principal Component Analysis: New Tools for Signal Processing and Machine Learning with Few and Faulty Training Data”

• M.S., Electronic & Computer Engineering, 2012
  Technical University of Crete (TUC), Chania, Crete, Greece
  Thesis title: “Full-rate Differential M-PSK Alamouti Modulation with Polynomial-complexity Maximum-likelihood Noncoherent Detection”

• Engineering Diploma (5-years), Electronic & Computer Engineering, 2010
  Technical University of Crete (TUC), Chania, Crete, Greece
  Thesis title: “Maximum-Likelihood Noncoherent M-PSK OSTBC Detection with Polynomial Complexity”

Employment

• Associate Professor and Cloud Technology Endowed Fellow 2025–present
  Depts. of Computer Engineering (CE) and Computer Science (CS)
  College of AI, Cyber and Computing (CAICC)
  The University of Texas at San Antonio
  San Antonio, TX
  Concurrent Roles:

  • Lead, Trustworthy AI Thrust, MATRIX: The UT San Antonio AI Consortium for Human Well-Being

  • Lead, AI Systems Research Thrust, Department of Computer Engineering

  • Founding Director, Machine Learning Optimization and Signal Processing Lab (MILOS)

  • Founding Co-Director, AI Systems Lab, Department of Computer Engineering

  • Faculty Member, PhD Program in Biomedical Engineering (joint program, UT San Antonio Main Campus and Health Science Center)

• Associate Professor and Margie and Bill Klesse Endowed Professor 2022–2025
  Depts. of Electrical & Computer Engineering (CE) and Computer Science (CS)
  Klesse College of Engineering and Integrated Design (KCEID)
  The University of Texas at San Antonio
  San Antonio, TX

• Associate Professor (with Tenure) 2021–2022
  Dept. of Electrical and Microelectronic Engineering (EME)
  Kate Gleason College of Engineering (KGCOE)
  Rochester Institute of Technology (RIT)
  Rochester, NY

• Assistant Professor (Tenure Track) 2015–2021
  Dept. of Electrical and Microelectronic Engineering (EME)
  Kate Gleason College of Engineering (KGCOE)
  Rochester Institute of Technology (RIT)
  Rochester, NY

• Visiting Summer Faculty (Independent Contractor) 2018, 2020, 2021
  Information Directorate (contract with Griffiss Institute Inc.)
  U.S. Air Force Research Laboratory (AFRL)
  Rome, NY

• Graduate Research Assistant 2011–2015
  Dept. of Electrical Engineering
  School of Engineering and Applied Sciences
  University at Buffalo (UB), The State University of New York
  Buffalo, NY

Memberships

Professional Society Memberships

• Senior Member, IEEE (Societies: Signal Processing, Computer, Education)

• Member, SPIE

• Member, ASEE

Honor Society Memberships (Elected)

• Member, IEEE-Eta Kappa Nu (IEEE-HKN), the Honor Society of IEEE

• Full Member, Sigma Xi, The Scientific Research Honor Society

Awards & Honors

Professional Honors and Major Awards

• Elected Full Member, Sigma Xi, The Scientific Research Honor Society, 2026

• Elected Member, IEEE-Eta Kappa Nu (IEEE-HKN), The Honor Society of IEEE, 2026

• Nominated for elevation to Fellow, SPIE, The International Society for Optics and Photonics, 2026

• Awarded MATRIX Summer Fellowship, UTSA AI Consortium for Human Well-Being, in support of leadership of the Trustworthy AI thrust and NSF AI Institute positioning, 2026

• Awarded the Cloud Technology Endowed Fellowship, Office of the Provost, UT San Antonio, September 2025

• Selected as UT San Antonio representative for the Faculty Research Immersive Program with Tec de Monterrey, June 2025

• Nominated for the University Excellence Award (Innovation and Impact), UT San Antonio, May 2025

• Awarded the Margie and Bill Klesse Endowed Professorship, Klesse College of Engineering and Integrated Design, UT San Antonio, 2022–2025 (three consecutive years)

• Awarded the UT System STARs Program Award, Board of Regents, University of Texas System, 2022

• Elevated to IEEE Senior Member, 2022

• Awarded the AFOSR Young Investigator Program (YIP) Award, U.S. Air Force Office of Scientific Research, 2019

• Awarded the Exemplary Performance in Research Award, Kate Gleason College of Engineering, Rochester Institute of Technology, 2019 (for proposals submitted in 2018)

• Awarded the Exemplary Performance in Teaching Award, Kate Gleason College of Engineering, Rochester Institute of Technology, 2019

• Awarded the Exemplary Performance in Research Award, Kate Gleason College of Engineering, Rochester Institute of Technology, 2018 (for proposals submitted in 2017)

• Awarded the Exemplary Reviewer Award, IEEE Communications Society, 2017

• Awarded the “Great Moment for Education” Award, Eurobank EFG (high-school top-scoring graduate in the Greek Panhellenic university admission examinations), October 2004

Paper Awards

• Awarded the Distinguished Conference Paper Award for “Robust Federated Learning via Stable Cosine Similarity,” IEEE International Carnahan Conference on Security Technology (ICCST), 2025

• Awarded the Runner-Up Poster Award for “Gait Recognition Based on Tensor Analysis of Acceleration Data from Wearable Sensors,” IEEE Western New York Image and Signal Processing Workshop, 2018

• Awarded a Student Travel Grant, SPIE Defense and Commercial Sensing, 2017 (for “Adaptive Sparse-Binary Waveform Design for All-Spectrum Channelization”)

• Awarded a Student Travel Grant, SPIE Defense, Security, and Sensing, 2014 (for “Direction Finding with L1-Norm Subspaces”)

• Awarded the Best Paper Award in Physical Layer Communications and Signal Processing for “Some Options for L1-Subspace Signal Processing,” IEEE International Symposium on Wireless Communication Systems (ISWCS), 2013

Selected Advisee Distinctions

• Ph.D. advisee Tien Nguyen received the Rising Star Award for our research on quantum computing, Quantum Student Success Summit, Rice University, March 2026

• Ph.D. advisee Dimitris G. Chachlakis received the Best Doctoral Dissertation Award (university-wide), Rochester Institute of Technology, April 2023

Publications

Publication metrics (as of June 10, 2026):

• Journals: 30 total; 6 under review

• Refereed Conference Papers: 65 total; 3 under review

• Total Citations: 1,790 (1,075 since 2021); H-index: 21; i10-index: 41

Journal Articles (Refereed)
Advisees are marked with ($^\dag$).

1. P. Markopoulos, “Continual Learning in Cybersecurity: Failure Modes and a Case for Managed Adaptation,” IEEE Aerosp. Electron. Syst. Mag., submitted June. 2026. Under review.

2. R. U. Haque$^\dag$, W.-M. Lin, and P. Markopoulos, “Federated Learning for Branch Prediction,” IEEE Trans. Comput., submitted Apr. 2026. Under review.

3. R. U. Haque$^\dag$ and P. Markopoulos, “FedAlign: Robust Federated Learning via Peer-Consensus Alignment,” IEEE Trans. Neural Net. Learn. Syst, submitted Jan. 2026. Under review.

4. V. T. Nguyen$^\dag$ and P. Markopoulos, “Quantum Adaptive Low-Rank CP Decomposition,” IEEE Trans. Quantum Eng., submitted Mar. 2026. Under review.

5. M. Dhanaraj$^\dag$, V. T. Nguyen$^\dag$, and P. Markopoulos, “Adaptive Low-Rank Multilinear Transformations for Compact Convolutional Neural Networks,” IEEE Trans. Artif. Intell., submitted Jan. 2026. Under review.

6. I. Tomeo$^\dag$, P. Markopoulos, and A. Savakis, “Quantum Annealing for Robust Principal Component Analysis,” IEEE Trans. Quantum Eng., pp. 1–11, Feb. 2026.
   DOI: 10.1109/TQE.2026.3661822

7. R. U. Haque$^\dag$ and P. Markopoulos, “Federated Learning With Automated Dual-Level Hyperparameter Tuning,” IEEE Open J. Signal Process., vol. 6, pp. 795–802, June 2025.
   DOI: 10.1109/OJSP.2025.3578273

8. M. Sharma$^\dag$, J. Heard, E. Saber, and P. Markopoulos, “Convolutional Neural Network Compression via Dynamic Parameter Rank Pruning,” IEEE Access, vol. 13, pp. 18441–18456, 2025.
   DOI: 10.1109/ACCESS.2025.3533419

9. S. Singh, E. Saber, P. Markopoulos, and J. Heard, “Regulating Modality Utilization within Multimodal Fusion Networks,” Sensors, vol. 24, no. 6054, Sept. 2024.
   DOI: 10.3390/s24186054

10. S. Colonnese, G. Scarano, M. Marra, P. Markopoulos, and D. A. Pados, “Joint Analysis and Segmentation of Time-varying Data with Outliers,” Digital Signal Processing (Elsevier), vol. 145, no. 104338, Feb. 2024.
    DOI: 10.1016/j.dsp.2023.104338

11. M. Dhanaraj$^\dag$ and P. Markopoulos, “On the Asymptotic L1-PC of Elliptical Distributions,” IEEE Signal Process. Lett., vol. 29, pp. 2343–2347, Sept. 2022.
    DOI: 10.1109/LSP.2022.3205274

12. S. Colonnese, P. Markopoulos, G. Scarano, and D. A. Pados, “FFT Calculation of the L1-norm Principal Component of a Data Matrix,” Signal Processing (Elsevier), vol. 189, no. 108286, Aug. 2021.
    DOI: 10.1016/j.sigpro.2021.108286

13. D. G. Chachlakis$^\dag$, T. Zhou, F. Ahmad, and P. Markopoulos, “Minimum Mean-Squared-Error Autocorrelation Processing in Coprime Arrays,” Digital Signal Processing (Elsevier), vol. 114, no. 103034, July 2021.
    DOI: 10.1016/j.dsp.2021.103034

14. D. G. Chachlakis$^\dag$, M. Dhanaraj$^\dag$, A. Prater-Bennette, and P. Markopoulos, “Dynamic L1-norm Tucker Tensor Decomposition,” IEEE J. Sel. Topics Signal Process., vol. 15, no. 3, pp. 587–602, Apr. 2021.
    DOI: 10.1109/JSTSP.2021.3058846

15. D. G. Chachlakis$^\dag$ and P. Markopoulos, “Structured Autocorrelation Matrix Estimation for Coprime Arrays,” Signal Processing (Elsevier), vol. 183, no. 107987, June 2021.
    DOI: 10.1016/j.sigpro.2021.107987

16. M. Sharma$^\dag$, M. Dhanaraj$^\dag$, D. G. Chachlakis$^\dag$, S. Karam, R. Ptucha, P. Markopoulos, and E. Saber, “YOLOrs: Object Detection in Multimodal Remote Sensing Imagery,” IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens., vol. 14, pp. 1497–1508, Nov. 2021.
    DOI: 10.1109/JSTARS.2020.3041316

17. H. Kamrani, A. Zoghadr Asli, P. Markopoulos, M. Langberg, D. A. Pados, and G. N. Karystinos, “Reduced-Rank L1-Norm Principal-Component Analysis with Performance Guarantees,” IEEE Trans. Signal Process., vol. 69, pp. 240–255, Nov. 2020.
    DOI: 10.1109/TSP.2020.3039599

18. S. A. Mamun, A. Ganguly, P. Markopoulos, M. Kwon, and A. Kwasinski, “NASCon: Network-Aware Server Consolidation for Server-Centric Wireless Datacenters,” Sustainable Computing: Informatics and Systems (Elsevier), vol. 29 (part A), no. 100452, Mar. 2021.
    DOI: 10.1016/j.suscom.2020.100452

19. D. G. Chachlakis$^\dag$, P. Markopoulos, and A. Prater-Bennette, “L1-Norm Tucker Tensor Decomposition,” IEEE Access, vol. 7, pp. 178454–178465, Nov. 2019.
    DOI: 10.1109/ACCESS.2019.2955134

20. Y. Liang, P. Markopoulos, and E. Saber, “Spatial-Spectral Segmentation of Hyperspectral Images for Subpixel Target Detection,” J. Appl. Remote Sens., vol. 13, no. 3, pp. 036502:1–036502:16, July 2019.
    DOI: 10.1117/1.JRS.13.036502

21. P. Markopoulos, N. Tsagkarakis, D. A. Pados, and G. N. Karystinos, “Realified L1-PCA for Direction-of-Arrival Estimation: Theory and Algorithms,” EURASIP J. Adv. Signal Process., no. 30, June 2019.
    DOI: 10.1186/s13634-019-0625-5

22. P. Markopoulos, S. Zlotnikov, and F. Ahmad, “Adaptive Radar-Based Human Activity Recognition with L1-norm Linear Discriminant Analysis,” IEEE J. Electromagn. RF Microw. Med. Biol., vol. 3, no. 2, pp. 120–126, June 2019.
    DOI: 10.1109/JERM.2019.2893587

23. P. Markopoulos, M. Dhanaraj$^\dag$ and A. Savakis, “Adaptive L1-Norm Principal-Component Analysis with Online Outlier Rejection,” IEEE J. Sel. Topics Signal Process., vol. 12, no. 6, pp. 1131–1143, Dec. 2018.
    DOI: 10.1109/JSTSP.2018.2874165

24. N. Tsagkarakis, P. Markopoulos, and D. A. Pados, “L1-Norm Principal-Component Analysis of Complex Data,” IEEE Trans. Signal Process., vol. 66, no. 12, pp. 3256–3267, June 2018.
    DOI: 10.1109/TSP.2018.2821641

25. P. Markopoulos, D. G. Chachlakis$^\dag$ and E. E. Papalexakis, “The Exact Solution to Rank-1 L1-Norm TUCKER2 Decomposition,” IEEE Signal Process. Lett., vol. 25, no. 4, pp. 511–515, Apr. 2018.
    DOI: 10.1109/LSP.2018.2790901

26. P. Markopoulos and G. N. Karystinos, “Noncoherent Alamouti Phase-Shift Keying with Full-Rate Encoding and Polynomial-Complexity Maximum-Likelihood Decoding,” IEEE Trans. Wireless Commun., vol. 16, no. 10, pp. 6688–6697, July 2017.
    DOI: 10.1109/TWC.2017.2728524

27. P. Markopoulos, S. Kundu, S. Chamadia, and D. A. Pados, “Efficient L1-Norm Principal-Component Analysis via Bit Flipping,” IEEE Trans. Signal Process., vol. 65, no. 16, pp. 4252–4264, Aug. 2017.
    DOI: 10.1109/TSP.2017.2708023

28. P. Markopoulos, G. N. Karystinos, and D. A. Pados, “Optimal Algorithms for L1-Subspace Signal Processing,” IEEE Trans. Signal Process., vol. 62, no. 19, pp. 5046–5058, Oct. 2014.
    DOI: 10.1109/TSP.2014.2338077

29. P. Markopoulos, S. Kundu, and D. A. Pados, “Small-Sample-Support Suppression of Interference to PN-Masked Data,” IEEE Trans. Commun., vol. 61, no. 7, pp. 2979–2987, July 2013.
    DOI: 10.1109/TCOMM.2013.043013.120643

30. A. Bletsas, A. Vlachaki, E. Kampianakis, G. Sklivanitis, J. Kimionis, K. Tountas, M. Asteris, and P. Markopoulos, “Building a Low-Cost Digital Garden as a Telecom Lab Exercise,” IEEE Pervasive Computing, vol. 12, no. 1, pp. 48–57, Jan. 2013.
    DOI: 10.1109/MPRV.2011.83

Conference Proceeding Articles (Refereed)
Advisees are marked with ($^\dag$). Presenter is marked with ($^\ddag$).

1. E. Moncada, J. G. Perez-Blanco, L. G. Hernández-Rojas, P. Markopoulos, and J. M. Antelis, “Modality Utilization in EEG–EMG Multimodal Neural Networks for Movement Detection,” in Proc. Mexican International Conference on Artificial Intelligence (MICAI 2026), 2026. Under review.

2. V. T. Nguyen$^{\dag,\ddag}$ and P. Markopoulos, “VQBR: Variational Quantum Bayesian Regression via Measurement-Based MAP Direction Recovery,” in Proc. IEEE Int. Conf. Quantum Comput. Eng. (QCE), 2026. Under review.

3. V. T. Nguyen$^{\dag,\ddag}$, M. Dhanaraj$^\dag$, and P. Markopoulos, “Depth- and Rank-Selective Multilinear Transformation Layers for Compact CNNs,” in Proc. 56th Asilomar Conf. Signals, Syst., Comput., 2026. Under review.

4. R. U. Haque$^{\dag,\ddag}$ and P. Markopoulos, “Robust Federated Learning via Stable Cosine Similarity,” in Proc. 2025 IEEE Int. Carnahan Conf. Security Technol. (ICCST), 2025.
   DOI: 10.1109/ICCST63435.2025.11295691
   $\star$ Distinguished Paper Award

5. R. U. Haque$^\dag$ and P. Markopoulos$^\ddag$, “LG-XLR: Loss-Guided Exponential Learning Rate Adaptation,” in Proc. 2025 IEEE Int. Conf. Digit. Signal Process. (DSP), pp. 1–5, 2025.
   DOI: 10.1109/DSP65409.2025.11075054

6. I. Tomeo$^{\dag,\ddag}$, P. Markopoulos, and A. Savakis, “Robust Interbattery Factor Analysis by Uniform Sample Contributions,” in Signal Process., Sensor/Information Fusion, and Target Recognition XXXIV, Proc. SPIE 13479, pp. 384–390, May 2025.
   DOI: 10.1117/12.3058624

7. D. Velychko$^\ddag$, S. Singh, P. Markopoulos, E. Saber, and J. Heard, “Image Preprocessing and YOLO Architectures for Enhanced Small and Slow-Moving Object Detection,” in Proc. 2024 IEEE WNY Image Signal Process. Workshop (WNYISPW), pp. 1–4, Nov. 2024.
   DOI: 10.1109/WNYISPW63690.2024.10786503

8. M. Krol, R. Hyder, M. Peechatt, A. Prater-Bennette, M. S. Asif, and P. Markopoulos$^\ddag$, “Continual Learning in Convolutional Neural Networks with Tensor Rank Updates,” in Proc. 2024 IEEE 13th Sensor Array Multichannel Signal Process. Workshop (SAM), pp. 1–5, 2024.
   DOI: 10.1109/SAM60225.2024.10636545

9. I. Tomeo$^{\dag,\ddag}$, P. Markopoulos, and A. Savakis, “L1-PCA with Quantum Annealing,” in Proc. SPIE 13036, Big Data VI: Learning, Analytics, and Applications, National Harbor, MD, Apr. 2024, p. 1303605.
   DOI: 10.1117/12.3015944

10. J. A. Sanchez Viloria$^\ddag$, D. Stripelis, P. Markopoulos, G. Sklivanitis, and D. A. Pados, “Adaptive Federated Learning for Automatic Modulation Classification Under Class and Noise Imbalance,” in Proc. AAAI 2024 Spring Symp. Series, Stanford, CA, Mar. 2024, p. 309.
    DOI: 10.1609/aaaiss.v3i1.31223

11. S. Singh$^\ddag$, P. Markopoulos, E. Saber, J. D. Lew, and J. Heard, “Measuring Modality Utilization in Multi-Modal Neural Networks,” in Proc. IEEE Conf. Artif. Intell. (IEEE CAI), Santa Clara, CA, Jun. 2023, pp. 11–14.
    DOI: 10.1109/CAI54212.2023.00014

12. S. Singh, M. Sharma$^\dag$, J. Heard, J. D. Lew, E. Saber, and P. Markopoulos$^{\ddag}$, “Multimodal Aerial View Object Classification with Disjoint Unimodal Feature Extraction and Fully-Connected-Layer Fusion,” in Proc. SPIE 12522, Big Data V: Learning, Analytics, and Applications, Orlando, FL, Apr. 2023, p. 1252206.
    DOI: 10.1117/12.2664041

13. I. Tomeo$^{\dag,\ddag}$ and P. Markopoulos, “HDA: An Iterative Hyperplane-Search Method for Discriminant Analysis,” in Proc. SPIE 12538, AI and Machine Learning for Multi-Domain Operations Applications V, Orlando, FL, Apr. 2023, p. 125381U.
    DOI: 10.1117/12.2664331

14. D. H. Le$^\ddag$ and P. Markopoulos, “Robust Singular Values Based on L1-norm PCA,” in Proc. IEEE Workshop Signal Process. Syst. (IEEE SiPS), Rennes, France, Nov. 2022, pp. 1–6.
    DOI: 10.1109/SiPS55645.2022.9919215

15. M. Dhanaraj$^{\dag,\ddag}$ and P. Markopoulos, “Robust Stochastic Principal Component Analysis via Barron Loss,” in Proc. IEEE Asilomar Conf. Signals, Syst., Comput. (IEEE ACSSC), Pacific Grove, CA, Oct. 2022, pp. 1286–1290.
    DOI: 10.1109/IEEECONF56349.2022.10051902

16. R. Hyder$^\ddag$, K. Shao, B. Hou, P. Markopoulos, A. Prater-Bennette, and M. S. Asif, “Incremental Task Learning with Incremental Rank Updates,” in Proc. Eur. Conf. Comput. Vision (ECCV), Tel Aviv, Israel, Oct. 2022, pp. 566–582.
    DOI: 10.1007978-3-031-20050-2_34

17. M. Sharma$^\dag$, P. Markopoulos$^\ddag$, E. Saber, M. S. Asif, and A. Prater-Bennette, “Convolutional Auto-Encoder with Tensor-Train Factorization,” in Proc. Int. Conf. Comput. Vision (ICCV), Montreal, QC, Canada, Oct. 2021, pp. 198–206.
    DOI: 10.1109/ICCVW54120.2021.00027

18. M. Mozaffari$^\ddag$, P. Markopoulos, and A. Prater-Bennette, “Improved L1-Tucker via L1-Fitting,” in Proc. Eur. Signal Process. Conf. (EUSIPCO), Dublin, Ireland, Aug. 2021, pp. 1075–1079.
    DOI: 10.23919/EUSIPCO54536.2021.9616014

19. M. Mozaffari$^\ddag$ and P. Markopoulos, “Robust Barron-Loss Tucker Tensor Decomposition,” in Proc. IEEE Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA, Oct. 2021, pp. 1651–1655.
    DOI: 10.1109/IEEECONF53345.2021.9723232

20. M. Sharma$^\dag$, P. Markopoulos$^\ddag$, and E. Saber, “YOLOrs-LITE: A Lightweight CNN for Real-time Object Detection in Remote Sensing,” in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), Brussels, Belgium, Jul. 2021, pp. 2604–2607.
    DOI: 10.1109/IGARSS47720.2021.9554418

21. D. G. Chachlakis$^\dag$ and P. Markopoulos$^\ddag$, “Novel Algorithms for Lp-quasi-norm Principal-Component Analysis,” in Proc. Eur. Signal Process. Conf. (EUSIPCO), Amsterdam, Netherlands, Jan. 2021, pp. 1045–1049.
    DOI: 10.23919/Eusipco47968.2020.9287335

22. G. Sklivanitis$^\ddag$, P. Markopoulos, D. A. Pados, and R. Diamant, “Robust Graph Localization for Underwater Acoustic Networks,” in Proc. IEEE Underwater Commun. Netw. Conf. (UComms), Lerici, Italy, Aug. 2021, pp. 1–5.
    DOI: 10.1109/UComms50339.2021.9598114

23. S. A. Mamun$^\ddag$, A. Ganguly, P. Markopoulos, A. Kwasinski, and M. Kwon, “What Can Ail Thee: New and Old Security Vulnerabilities of Wireless Datacenters,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Taipei, Taiwan, Dec. 2020, pp. 1–7.
    DOI: 10.1109/GLOBECOM42002.2020.9322619

24. Y. Tsitsikas$^\ddag$, D. G. Chachlakis$^\dag$, E. Papalexakis, and P. Markopoulos, “L1-Norm RESCAL Decomposition,” in Proc. IEEE Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA, Nov. 2020, pp. 940–944.
    DOI: 10.1109/IEEECONF51394.2020.9443401

25. D. Chachlakis$^\ddag$, A. Prater-Bennette, and P. Markopoulos, “L1-Norm Higher-order Orthogonal Iterations for Robust Tensor Analysis,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Barcelona, Spain, May 2020, pp. 4826–4830.
    DOI: 10.1109/ICASSP40776.2020.9053701

26. M. Dhanaraj$^{\dag,\ddag}$, M. Sharma$^\dag$, T. Sarkar, S. Karnam, D. G. Chachlakis$^\dag$, R. Ptucha, P. Markopoulos, and E. Saber, “Vehicle Detection from Multi-modal Aerial Imagery Using YOLOv3 with Mid-level Fusion,” in Proc. SPIE 11395, Big Data II: Learning, Analytics, and Applications, Anaheim, CA, Apr. 2020, p. 1139506.
    DOI: 10.1117/12.2558115

27. K. Tountas$^\ddag$, D. G. Chachlakis$^\dag$, P. Markopoulos, and D. A. Pados, “Iteratively Re-weighted L1-PCA of Tensor Data,” in Proc. IEEE Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA, Oct. 2019, pp. 1658–1661.
    DOI: 10.1109/IEEECONF44664.2019.9048775

28. D. G. Chachlakis$^{\dag,\ddag}$ and P. Markopoulos, “Combinatorial Search for the Lp-Norm Principal Component of a Matrix,” in Proc. IEEE Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA, Oct. 2019, pp. 1611–1615.
    DOI: 10.1109/IEEECONF44664.2019.9048980

29. S. A. Mamun$^\ddag$, A. Ganguly, M. Kwon, A. Kwasinski, and P. Markopoulos, “Network-Aware Server Consolidation for Wireless Data Centers,” in Proc. Int. Conf. Netw. Future (NoF), Rome, Italy, Oct. 2019, pp. 58–65.
    DOI: 10.1109/NoF47743.2019.9014979

30. D. Chachlakis$^\ddag$, Y. Tsitsikas, E. Papalexakis, and P. Markopoulos, “Robust Multi-Relational Learning with Absolute Projection RESCAL,” in Proc. IEEE Global Conf. Signal Inf. Process. (GlobalSIP), Ottawa, ON, Canada, Nov. 2019, pp. 1–5.
    DOI: 10.1109/GlobalSIP45357.2019.8969097

31. M. Dhanaraj$^{\dag,\ddag}$ and P. Markopoulos, “Stochastic Principal Component Analysis via Mean Absolute Projection Maximization,” in Proc. IEEE Global Conf. Signal Inf. Process. (GlobalSIP), Ottawa, ON, Canada, Nov. 2019, pp. 1–5.
    DOI: 10.1109/GlobalSIP45357.2019.8969411

32. G. Orru$^\ddag$, T. Cattai, S. Colonnese, G. Scarano, F. De Vico Fallani, P. Markopoulos, and D. A. Pados, “Deep L1-PCA of Time-Variant Data with Application to Brain Connectivity Measurements,” in Proc. Eur. Signal Process. Conf. (EUSIPCO), A Coruna, Spain, Sep. 2019, pp. 1–5.
    DOI: 10.23919/EUSIPCO.2019.8903169

33. D. G. Chachlakis$^{\dag,\ddag}$, M. Dhanaraj$^\dag$, P. Markopoulos, and A. Prater-Bennette, “Options for Multimodal Classification Based on L1-Tucker Decomposition,” in Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, Baltimore, MD, Apr. 2019, 109890O.
    DOI: 10.1117/12.2520140

34. S. Zlotnikov$^\ddag$, P. Markopoulos, and F. Ahmad, “Incremental L1-Norm Linear Discriminant Analysis for Indoor Human Activity Classification,” in Proc. IEEE Radar Conf. (RadarConf), Boston, MA, Apr. 2019, pp. 1–4.
    DOI: 10.1109/RADAR.2019.8835593

35. P. Markopoulos$^{\ddag}$, D. G. Chachlakis$^\dag$, and A. Prater-Bennette, “L1-Norm Higher-Order Singular-Value Decomposition,” in Proc. IEEE Global Conf. Signal Inf. Process. (GlobalSIP), Anaheim, CA, Nov. 2018, pp. 1353–1357.
    DOI: 10.1109/GlobalSIP.2018.8646384

36. K. Bichave$^\ddag$, O. Brewer, M. Gusinov, P. Markopoulos, and I. Puchades, “Gait Recognition Based on Tensor Analysis of Acceleration Data from Wearable Sensors,” in Proc. IEEE Western New York Image Signal Process. Workshop (WNYISPW), Rochester, NY, Oct. 2018, pp. 1–5.
    DOI: 10.1109/WNYIPW.2018.8576383
    $\star$ Best Poster Award Runner-up

37. M. Dhanaraj$^{\dag,\ddag}$, D. G. Chachlakis$^\dag$, and P. Markopoulos, “Incremental Complex L1-PCA for Direction-of-Arrival Estimation,” in Proc. IEEE Western New York Image Signal Process. Workshop (WNYISPW), Rochester, NY, Oct. 2018, pp. 1–5.
    DOI: 10.1109/WNYIPW.2018.8576444

38. A. Gannon$^\ddag$, G. Sklivanitis, P. Markopoulos, D. A. Pados, and S. N. Batalama, “Semi-Blind Signal Recovery in Impulsive Noise with L1-Norm PCA,” in Proc. IEEE Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA, Oct. 2018, pp. 477–481.
    DOI: 10.1109/ACSSC.2018.8645514

39. M. Dhanaraj$^{\dag,\ddag}$ and P. Markopoulos, “Novel Algorithm for Incremental L1-Norm Principal-Component Analysis,” in Proc. Eur. Signal Process. Conf. (EUSIPCO), Rome, Italy, Sep. 2018, pp. 2020–2024.
    DOI: 10.23919/EUSIPCO.2018.8553234

40. P. Markopoulos$^{\ddag}$ and F. Ahmad, “Robust Radar-Based Human Motion Recognition with L1-Norm Linear Discriminant Analysis,” in Proc. IEEE Int. Microw. Biomed. Conf. (IMBioC), Philadelphia, PA, Jun. 2018, pp. 145–147.
    DOI: 10.1109/IMBIOC.2018.8428927

41. D. G. Chachlakis$^{\dag,\ddag}$, P. Markopoulos, and F. Ahmad, “MMSE-Based Autocorrelation Sampling for Coprime Arrays,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Calgary, AB, Canada, Apr. 2018, pp. 3474–3478.
    DOI: 10.1109/ICASSP.2018.8461676

42. D. G. Chachlakis$^{\dag,\ddag}$ and P. Markopoulos, “Novel Algorithms for Exact and Efficient L1-Norm-Based TUCKER2 Decomposition,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Calgary, AB, Canada, Apr. 2018, pp. 6294–6298.
    DOI: 10.1109/ICASSP.2018.8461834

43. D. G. Chachlakis$^{\dag,\ddag}$ and P. Markopoulos, “Robust Decomposition of 3-Way Tensors Based on L1-Norm,” in Proc. SPIE 10658, Compressive Sensing VII, Orlando, FL, Apr. 2018, p. 1065807.
    DOI: 10.1117/12.2307844

44. S. Zlotnikov$^\ddag$, P. Somaru, P. Markopoulos, and F. Ahmad, “A Linear Discriminative Analysis Based Fall Motion Detector Using Radar,” in Proc. SPIE 10658, Compressive Sensing VII, Orlando, FL, Apr. 2018, p. 106580D.
    DOI: 10.1117/12.2311574

45. D. G. Chachlakis$^{\dag,\ddag}$, P. Markopoulos, and F. Ahmad, “The Mean-Squared-Error of Autocorrelation Sampling in Coprime Arrays,” in Proc. IEEE Int. Workshop Comput. Adv. Multi-Sensor Adapt. Process. (CAMSAP), Curacao, Dutch Antilles, Dec. 2017, pp. 1–5.
    DOI: 10.1109/CAMSAP.2017.8313121

46. P. Markopoulos$^{\ddag}$ and F. Ahmad, “Indoor Human Motion Classification by L1-Norm Subspaces of Micro-Doppler Signatures,” in Proc. IEEE Radar Conf. (RadarConf), Seattle, WA, May 2017, pp. 1807–1810.
    DOI: 10.1109/RADAR.2017.7944504

47. P. Markopoulos$^{\ddag}$, D. A. Pados, G. N. Karystinos, and M. Langberg, “L1-Norm Principal-Component Analysis in L2-Norm-Reduced-Rank Data Subspaces,” in Proc. SPIE 10211, Compressive Sensing VI, Anaheim, CA, Apr. 2017, p. 1021104.
    DOI: 10.1117/12.2263733

48. D. G. Chachlakis$^{\dag,\ddag}$, P. Markopoulos, R. J. Muchhala, and A. Savakis, “Visual Tracking with L1-Grassmann Manifold Modeling,” in Proc. SPIE 10211, Compressive Sensing VI, Anaheim, CA, Apr. 2017, p. 1021102.
    DOI: 10.1117/12.2263691

49. G. Sklivanitis$^\ddag$, P. Markopoulos, S. Batalama, and D. A. Pados, “Adaptive Sparse-Binary Waveform Design for All-Spectrum Channelization,” in Proc. SPIE 10211, Compressive Sensing VI, Anaheim, CA, Apr. 2017, p. 102110B.
    $\star$ Student Travel Grant Award

50. P. Markopoulos$^{\ddag}$, “Linear Discriminant Analysis with Few Training Data,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), New Orleans, LA, Mar. 2017, pp. 4626–4630.
    DOI: 10.1109/ICASSP.2017.7953033

51. G. Sklivanitis$^\ddag$, P. Markopoulos, S. Batalama, and D. A. Pados, “Sparse Waveform Design for All-Spectrum Channelization,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), New Orleans, LA, Mar. 2017, pp. 3764–3768.
    DOI: 10.1109/ICASSP.2017.7952860

52. N. Tsagkarakis$^\ddag$, P. Markopoulos, and D. A. Pados, “On the L1-norm Approximation of a Matrix by Another of Lower Rank,” in Proc. IEEE Int. Conf. Mach. Learn. Appl. (ICMLA), Anaheim, CA, Dec. 2016, pp. 768–773.
    DOI: 10.1109/ICMLA.2016.0137

53. P. Markopoulos$^{\ddag}$, S. Kundu, S. Chamadia, and D. A. Pados, “L1-Norm Principal-Component Analysis via Bit Flipping,” in Proc. IEEE Int. Conf. Mach. Learn. Appl. (ICMLA), Anaheim, CA, Dec. 2016, pp. 326–332.
    DOI: 10.1109/ICMLA.2016.0060

54. P. Markopoulos$^{\ddag}$, N. Tsagkarakis, D. A. Pados, and G. N. Karystinos, “Direction-of-Arrival Estimation from L1-Norm Principal Components,” in Proc. IEEE Int. Symp. Phased Array Syst. Technol. (PAST), Waltham, MA, Oct. 2016, pp. 1–6.
    DOI: 10.1109/ARRAY.2016.7832585

55. Y. Liang$^\ddag$, P. Markopoulos, and E. Saber, “Subpixel Target Detection in Hyperspectral Images with Local Matched Filtering in SLIC Superpixels,” in Proc. IEEE Workshop Hyperspectral Image Signal Process. (WHISPERS), Los Angeles, CA, Aug. 2016, pp. 1–5.
    DOI: 10.1109/WHISPERS.2016.8071719

56. P. Markopoulos$^{\ddag}$, “Reduced-Rank Filtering on L1-Norm Subspaces,” in Proc. IEEE Sensor Array Multichannel Signal Process. Workshop (SAM), Rio de Janeiro, Brazil, Jul. 2016, pp. 1–5.
    DOI: 10.1109/SAM.2016.7569743

57. Y. Liang$^\ddag$, P. Markopoulos, and E. Saber, “Subpixel Target Detection in Hyperspectral Images from Superpixel Background Statistics,” in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), Beijing, China, Jul. 2016, pp. 7018–7021.
    DOI: 10.1109/IGARSS.2016.7730830

58. P. Markopoulos$^{\ddag}$, S. Kundu, and D. A. Pados, “L1-Fusion: Robust Linear-Time Image Recovery from Few Severely Corrupted Copies,” in Proc. IEEE Int. Conf. Image Process. (ICIP), Quebec City, QC, Canada, Sep. 2015, pp. 1225–1229.
    DOI: 10.1109/ICIP.2015.7350995

59. N. Tsagkarakis$^\ddag$, P. Markopoulos, and D. A. Pados, “Direction Finding by Complex L1-Principal Component Analysis,” in Proc. IEEE Int. Workshop Signal Process. Adv. Wireless Commun. (SPAWC), Stockholm, Sweden, Jun. 2015, pp. 475–479.
    DOI: 10.1109/SPAWC.2015.7227083

60. P. Markopoulos$^{\ddag}$, N. Tsagkarakis, D. A. Pados, and G. N. Karystinos, “Direction Finding with L1-Norm Subspaces,” in Proc. SPIE 9109, Compressive Sensing III, Baltimore, MD, May 2014, p. 91090J.
    DOI: 10.1117/12.2053049
    $\star$ Student Travel Grant Award

61. S. Kundu$^\ddag$, P. Markopoulos, and D. A. Pados, “Fast Computation of the L1-Principal Component of Real-Valued Data,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Florence, Italy, May 2014, pp. 8028–8032.
    DOI: 10.1109/ICASSP.2014.6855164

62. P. Markopoulos$^{\ddag}$, G. N. Karystinos, and D. A. Pados, “Some Options for L1-Subspace Signal Processing,” in Proc. IEEE Int. Symp. Wireless Commun. Syst. (ISWCS), Ilmenau, Germany, Aug. 2013, pp. 622–626.
    $\star$ Best Paper Award in Physical Layer Communications

63. P. Markopoulos$^{\ddag}$ and G. N. Karystinos, “Novel Full-Rate Noncoherent Alamouti Encoding that Allows Polynomial-Complexity Optimal Decoding,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Vancouver, BC, Canada, May 2013, pp. 5075–5079.
    DOI: 10.1109/ICASSP.2013.6638628

64. P. Markopoulos$^{\ddag}$, S. Kundu, and D. A. Pados, “Short-Data-Record Filtering of PN-Masked Data,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Vancouver, BC, Canada, May 2013, pp. 4559–4563.
    DOI: 10.1109/ICASSP.2013.6638523

65. A. Bletsas, A. Vlachaki, E. Kampianakis$^\ddag$, G. Sklivanitis, J. Kimionis, K. Tountas, M. Asteris, and P. Markopoulos, “Towards Precision Agriculture: Building a Soil Wetness Multi-Hop WSN from First Principles,” in Proc. Int. Workshop Sensing Technol. Architecture, Forestry Environ. (ECOSENSE), Belgrade, Serbia, Apr. 2011, pp. 1–4. 7)

Book Chapters (Invited, Non-Refereed)

1. D. G. Chachlakis$^\dag$, M. Dhanaraj$^\dag$, P. Markopoulos, and A. Prater-Bennette, “Dynamic and Robust Analysis of Tensor Data in the DDDAS Framework,” in Handbook of Dynamic Data-Driven Application Systems (Vol. III), E. P. Blasch, F. Darema, S. Ravela, and A. J. Aved, Eds. Springer, Cham, Jun. 2026.
   ISBN: 978-3-031-88573-0

2. F. Ahmad and P. Markopoulos, “L1-Norm Principal Component and Discriminant Analyses of Micro-Doppler Signatures for Indoor Human Activity Recognition,” in Micro-Doppler Radar and its Applications, F. Fioranelli, M. Ritchie, A. Balleri, and H. Griffiths, Eds. IET Press, 2020.
   DOI: 10.1049/SBRA531E_ch11

3. P. Markopoulos, S. Kundu, S. Chamadia, N. Tsagkarakis, and D. A. Pados, “Outlier-Resistant Data Processing with L1-norm Principal Component Analysis,” in Advances in Principal Component Analysis: Research and Development, G. R. Naik, Ed. Springer, 2018.
   DOI: 10.1007/978-981-10-6704-4_6

Talks & Tutorials

Refereed Conference Presentations
All presentations associated with refereed conference papers are listed in detail above, under subsection “Conference Proceeding Articles (Refereed)”, where the presenter is marked with ($^\ddag$).

Invited Talks

1. P. Markopoulos, “Tensor Methods for Efficient, Robust, and Continual Machine Learning,” presented at GRDS-SPIE Seminar, Univ. of Texas at Rio Grande Valley, Apr. 2026.

2. P. Markopoulos, “Towards Trustworthy and Privacy-Preserving Federated Learning: Methods, Challenges, and Applications,” presented at Biomedical Engineering (BME) Seminar Series, UT San Antonio, San Antonio, TX, Oct. 2025.

3. P. Markopoulos, “Quantum Computing at The University of Texas at San Antonio,” presented at First Texas Quantum Summit, Texas A&M Univ., College Station, TX, Sept. 2025.

4. P. Markopoulos, “Advances in Robust and Efficient Machine Learning for National Security,” presented at Dept. of Defense (16th Air Force / TD), Aug. 2023.

5. P. Markopoulos, “Tensor Methods for Efficient, Robust, and Continual Machine Learning,” presented at MATRIX AI Seminar Series, UT San Antonio, San Antonio, TX, Sept. 2022.

6. P. Markopoulos, “Advances in Robust Machine Learning,” presented at Intelligence Community Academic Research Symposium (ICARS), National Academies of Sciences, Engineering, and Medicine, Sept. 2022.

7. P. Markopoulos, “Advances in Robust Machine Learning,” presented at ICARS, National Academies of Sciences, Engineering, and Medicine, Sept. 2021.

8. P. Markopoulos, “L1-norm PCA: Algorithms and Applications,” presented at Graduate Seminar, RIT, Rochester, NY, May 2021.

9. P. Markopoulos, “Tensor Methods for Imaging Science,” presented at Center for Imaging Science, RIT, Rochester, NY, Nov. 2020.

10. P. Markopoulos, “Tensor Methods Based on Absolute Projections,” presented at Applied Mathematics Seminar, Syracuse Univ., Syracuse, NY, Oct. 2020.

11. P. Markopoulos, “Improving CNNs Towards Real-Time Multi-Modal Object Detection in Remote Sensing Imagery,” presented at ICARS, National Academies of Sciences, Engineering, and Medicine, Sept. 2020.

12. P. Markopoulos, “Tensor Methods for Image Processing,” presented at SIAM IS20, Tensor Methods for Image Processing Mini-Symposium, Toronto, Canada, July 2020.

13. P. Markopoulos, “Improving CNNs Towards Real-Time Multi-Modal Object Detection in Remote Sensing Imagery,” presented at National Geospatial-Intelligence Agency (NGA), July 2020.

14. P. Markopoulos, “Tensor Methods for Robust Machine Learning,” presented at U.S. Air Force Research Laboratory (AFRL/RI), Rome, NY, Apr. 2020.

15. P. Markopoulos, “Robust Subspace Learning and Applications in Computer Vision,” presented at ICCV Workshop on Robust Subspace Learning, Seoul, Korea, Oct. 2019.

16. P. Markopoulos, “L1-norm PCA: Algorithms and Applications,” presented at U.S. Air Force Research Laboratory (AFRL), Rome, NY, June 2018.

17. P. Markopoulos, “Tensor Methods for Imaging and Sensing,” presented at Institute for Sensing and Embedded Network Systems Engineering (I-SENSE), Florida Atlantic Univ., Boca Raton, FL, May 2018.

18. P. Markopoulos, “Robust Subspace Processing,” presented at MSEE Graduate Seminar, RIT, Rochester, NY, Apr. 2018.

19. P. Markopoulos, “L1-norm Methods for Signal Processing and Machine Learning,” presented at Move78 / AI and Cognitive Technologies Speaker Series, RIT, Rochester, NY, Jan. 2018.

20. P. Markopoulos, “Robust Subspace Processing and Machine Learning,” presented at Univ. of Rochester (IEEE Rochester Section, AES/COMSOC), Rochester, NY, Nov. 2016.

21. P. Markopoulos, “L1-norm Tensor Methods Based on Absolute Projections,” presented at Dept. of Electrical and Microelectronic Engineering, RIT, Rochester, NY, Mar. 2015.

22. P. Markopoulos, “Tensor Methods for Communications and Signal Processing,” presented at Dept. of Electrical and Computer Engineering, Northeastern Univ., Boston, MA, Oct. 2014.

Tutorials and Short Courses

1. P. Markopoulos, “Federated Learning for Decentralized and Privacy-Preserving Machine Learning,” tutorial presented at SPIE DCS 2025, Machine Learning from Challenging Data Conf., Apr. 2025.

2. P. Markopoulos, “Federated Learning in Healthcare: A Brief Review of Foundations, Applications, Challenges, and Opportunities,” tutorial presented at NSF AI Spring School, UT San Antonio, San Antonio, TX, Feb. 2025.

3. P. Markopoulos, “Federated Learning: Introductory Tutorial,” tutorial presented at Dynamic Data-Driven Application Systems (DDDAS), Nov. 2024.

4. P. Markopoulos, “Tensor Methods for Signal Processing and Machine Learning,” tutorial presented at Summer School on Data Science, Technical Univ. of Crete, Chania, Greece, Aug. 2020.

5. P. Markopoulos, “L1-norm Principal Component Analysis of Multi-Modal Data,” tutorial presented at Summer School, Technical Univ. of Crete, Chania, Greece, July 2018.

Other Presentations

1. P. Markopoulos, “L1-norm Methods for Communications and Signal Processing,” poster presented at IEEE North American School of Information Theory, Toronto, Canada, June 2014.

Patents

• Patent on quantum-classical computing, internal innovation disclosure submitted, May 2026.

Funding

Pending Proposals Available upon request.

Current Grants

• Title: “PARTNER: Neuro-Inspired AI for the Edge at UTSA (NAIAD)”; Sponsor: National Science Foundation (NSF); Scope: Research; Total: $2,800,000; Own share: $420,000 (15%); Dates: Awarded Sep 18, 2023; Role: Co-PI

• Title: “AI/ML Models Designed to Improve the Quality of Life for Individuals with Disabilities”; Sponsor: University of North Texas Health Science Center at Fort Worth; Scope: Research; Total: $100,000; Own share: $30,000 (30%); Dates: Awarded May 1, 2025; Role: Co-PI

Previous Grants

• Title: “EXAIL: NSF ExpandAI Leadership Workshop”; Sponsor: National Science Foundation (NSF); Scope: Research, Outreach; Total: $137,071; Own share: $68,536 (50%); Dates: Aug. 2, 2024 – Aug. 2025; Role: Co-PI

• Title: “MATCH: The MATRIX AI/ML Concierge for Healthcare”; Sponsor: The University of North Texas Health Science Center at Fort Worth (via MATRIX AI Consortium); Scope: Research; Total: $500,000; Own share: (no fixed share; funding spent at UTSA as Senior Personnel); Dates: Sep. 2024 – Sep. 2025; Role: Senior Personnel

• Title: “M-POWER: MATRIX-Provided AI/ML Open-Source Resource Center for Behavioral Health Empowerment”; Sponsor: The University of North Texas Health Science Center at Fort Worth (via MATRIX AI Consortium); Scope: Research; Total: $500,000; Own share: $50,000 (10%); Dates: Nov. 16, 2023 – Aug. 2024; Role: Senior Personnel

• Title: “Theory and Efficient Algorithms for Dynamic and Robust L1-Norm Analysis of Tensor Data” [AFOSR Young Investigator Program (YIP)]; Sponsor: U.S. Air Force Office of Scientific Research (AFOSR) [originally awarded to RIT; balance transferred to UT San Antonio]; Scope: Research; Total: $348,460; Own share: $348,460 (100%); Dates: Jan. 2020 – May 2024; Role: Sole PI.
  Balance transferred to UT San Antonio: Balance transfer: $195,401; Own share: $195,401 (100%); Dates: Dec. 15, 2022 – May 2024; Role: Sole PI

• Title: “Target Detection/Tracking and Activity Recognition from Multimodal Data”; Sponsor: National Geo-spatial Intelligence Agency; Scope: Research; Total: $858,534; Own share: $343,414 (40%); Dates: Sep. 2019 – Sep. 2024; Role: Co-PI
  Balance transferred to UT San Antonio: Balance transfer: $10,000; Own share: $10,000 (100%); Dates: Sep. 25, 2023 – May 2024; Role: Sole PI

• Title: “Efficient Radar Imaging and Machine Learning for Concealed Object Detection”; Sponsor: U.S. Air Force Research Laboratory (USAF); Scope: Research; Total: $58,079; Own share: $58,079 (100%); Dates: Oct. 2021 – Jun. 2022; Role: Sole PI

• Title: “Continual and Incremental Learning with Tensor-Factorized Neural Networks”; Sponsor: U.S. Air Force Research Laboratory (USAF); Scope: Research; Total: $30,286; Own share: $30,286 (100%); Dates: Sep. 2021 – Dec. 2021; Role: Sole PI

• Title: “Data-Driven Adaptive Learning for Video Analytics”; Sponsor: U.S. Air Force Research Laboratory (USAF); Scope: Research; Total: $352,152; Own share: $142,000 (40%); Dates: Feb. 2018 – Feb. 2021; Role: Co-PI

• Title: “Efficient Methods for Dynamic and Robust Analysis of Tensors”; Sponsor: U.S. Air Force Research Laboratory (USAF); Scope: Research; Total: $9,999; Own share: $9,999 (100%); Dates: Nov. 2020 – Dec. 2020; Role: Sole PI

• Title: “Development and Testing of Robust Algorithms for Real-Time Recognition of Complex Gait Patterns from Wearable Sensor Data”; Sponsor: Rochester Institute of Technology / KGCOE FEAD; Scope: Research; Total: $21,000; Own share: $10,500 (50%); Dates: May 2018 – Dec. 2019; Role: PI

• Title: “Methods for Corruption-Resistant Analysis of Tensor Data”; Sponsor: U.S. Air Force Research Laboratory (USAF); Scope: Research; Total: $15,000; Own share: $15,000 (100%); Dates: Sep. 2018 – Oct. 2018; Role: Sole PI

• Title: “Distributed Self-Localization of Wireless-Node Squads in Hostile Environments”; Sponsor: Harris Corporation; Scope: Research; Total: $60,000; Own share: $40,000 (67%); Dates: Nov. 2016 – Jun. 2017; Role: PI

• Title: “Practical L1-Norm Principal Component Analysis: Tools for Reliable Data Analytics”; Sponsor: Rochester Institute of Technology (OVPR / GWBC 2016); Scope: Research; Total: $5,000; Own share: $5,000 (100%); Dates: Apr. 2016 – Mar. 2017; Role: Sole PI

Teaching

Career-wide teaching metrics (enrollment-weighted averages, as of June 10, 2026):

• Course rating: $\sim$ 4.34/5

• Instructor rating: $\sim$ 4.40/5

Below, “YYYY-T (E)” denotes year ‘YYYY’, term ‘T’, and enrollment (over all sections) ‘E’
Courses at UT San Antonio (2022–present)

• Machine Learning (EE 4463 / EE 5573 / CS 6243; undergraduate/graduate cross-listed). Offered in: 2026-S (33), 2025-F (18), 2025-S (43), 2024-F (29), 2022-F (8). Enrollment-weighted mean course rating (excluding 2022-F with no evaluations): 4.51/5.

• Advanced Machine Learning (CS 6243 / EE 6363; graduate). Offered in: 2023-F (22). Course rating: 4.67/5.

• Probability and Stochastic Processes (EE 3533; undergraduate). Offered in: 2023-S (22). Course rating: 4.50/5.

Courses at RIT (2015–2022)

• Introduction to Communication Systems (EEEE-484; undergraduate). Offered in: 2019-F (64), 2020-S (55). Enrollment-weighted mean course rating: 4.10/5.

• Digital Data Communications (EEEE-593 / EEEE-693; undergraduate/graduate cross-listed). Offered in: 2015-F (24), 2016-F (25), 2018-S (18), 2019-S (19). Enrollment-weighted mean course rating: 4.23/5.

• Communication Networks (EEEE-592 / EEEE-692; undergraduate/graduate cross-listed). Offered in: 2016-S (28), 2017-S (30), 2017-F (20), 2018-F (20). Enrollment-weighted mean course rating: 4.35/5.

• Sensor Array Processing for Wireless Communications (EEEE-594 / EEEE-694; undergraduate/graduate cross-listed; newly developed course). Offered in: 2018-S (19), 2019-S (14), 2020-S (11). Enrollment-weighted mean course rating: 4.36/5.

• Optimization Methods for Engineers (EEEE-595 / EEEE-695; undergraduate/graduate; newly developed course). Developed and approved by department and college; not offered.

• Freshman Practicum (EEEE-105; undergraduate). Offered in: 2017-F (15).

• Cooperative Education (EEEE-499). Supervised student co-op placements every term from 2017-S through 2022-S.

Courses at UB (before 2015, as Graduate Student)

• Smart Antennas (EE 614; graduate). Invited lectures in: 2014-S.

• Communication Systems II (EE 484; undergraduate). Invited lectures in: 2013-F.

Student Mentoring

Doctoral Advisees

Current

• L. Davis, Ph.D. in Computer Science, UT San Antonio, Jan. 2026–present
  Research: Continual learning theory and methods

• R. U. Haque, Ph.D. in Electrical Engineering, UT San Antonio, Aug. 2023–present
  Research: Robust federated learning in non-heterogeneous environments
  Received Distinguished Paper Award at IEEE ICCST

• V. T. Nguyen, Ph.D. in Electrical Engineering, UT San Antonio, Aug. 2023–present
  Research: Theory and methods for hybrid quantum-classical computing
  Received Rising Star Award at 2026 Quantum Student Success Summit at Rice University

• I. Tomeo, Ph.D. in Engineering, RIT, Aug. 2020–present
  Research: Quantum computing methods for machine learning
  Co-advised with Dr. A. Savakis

Graduated

• M. Sharma, Ph.D. in Imaging Science, RIT, 2024.
  Dissertation: Incremental and continual learning with deep neural networks
  Co-advised with Dr. E. Saber
  First employment: R&D Engineer, Digimarc Inc.

• M. Dhanaraj, Ph.D. in Engineering, RIT, 2022
  Dissertation: Stochastic subspace optimization and tensors for deep learning
  First employment: Applied Scientist II, Amazon.com Inc.

• D. Chachlakis, Ph.D. in Electrical and Computer Engineering, RIT, 2021
  Dissertation: Theory and algorithms for reliable multimodal data analysis, machine learning, and signal processing
  First graduate of Ph.D. in ECE program, RIT
  Received Best Doctoral Dissertation Award (university-wide), RIT, April 2023
  First employment: Senior R&D Engineer, Digimarc Inc.

• Y. Liang, Ph.D. in Imaging Science, RIT, 2019
  Dissertation: Object detection in high-resolution aerial and hyperspectral imagery
  Co-advised with Dr. E. Saber
  First employment: Video Quality Engineer, Apple Inc.

• M. Mozaffari, partial advising in Ph.D. in Electrical and Computer Engineering, RIT, from 2020 to 2022
  First employment: Applied Scientist II, Amazon.com Inc.

M.S. and B.S. Advisees

Current

• B. Millis, BBA-Cybersecurity, UT San Antonio, 2026

• S. M. Hossein, B.S., Computer Engineering, UT San Antonio, 2025

Graduated

Over 25 past M.S./B.S. advisees, working on theses, graduate papers, volunteering, funded research assistantships.

Thesis/Dissertation Committees

Over 30 graduate/doctoral thesis/dissertation committees.

Service

Leadership and Service at UT San Antonio

University Level

• Founding Director, UT San Antonio Machine Learning Optimization and Systems (MILOS) Laboratory, 2022–present

• Lead, Trustworthy AI Thrust, MATRIX AI Consortium for Human Well-Being, UT San Antonio, 2024–present

• Thrust Lead for Quantum Algorithms, Computing, and Machine Learning, QuICR university strategic proposal/initiative, 2025–present

• Lead, Cluster Hire Proposal in “Machine Learning Hardware and Systems,” Office of Academic Affairs, May 2025

• Co-Author, UT San Antonio institutional response to the 2026 DARPA AI-FORGE program (RFI), June 2026

• Co-Author, UT San Antonio institutional response to the 2025 National AI R&D Strategic Plan (RFI), May 2025

• Faculty Mentor to Dr. Dharanidhar Dang, 2025–present

• Member, CAICC Founding Dean Search Committee (interim and national searches; one of  25 faculty members across UTSA and UT Health San Antonio), 2025–March 2026

• Member, CAICC Planning Advisory Task Force (one of  30 faculty members across UTSA and UT Health San Antonio), 2023–2024

• Member, Regents’ Professorships Committee, MATRIX AI Cluster, 2025–2026

• Member, Regents’ Professorships Cluster Hiring Committee (AI), 2024–2025

• Member, Hiring Committee for Critical Infrastructure Security / Cyber-Physical Systems (Job ID 14343), RREP Cybersecurity Cluster, CAICC, 2026

• Member, Hiring/Search Committee for Host-Microbe Interactions, Data Analytics and Public Health Cluster (Job ID 14382), CCP, CAICC and Colleges of Sciences and Health, Community and Policy, 2026

• Member, Hiring Committee for Secure Cyber Infrastructure (Job ID 14338), RREP Cybersecurity Cluster, CAICC, 2026

• Member, Hiring/Search Committee for AI Security (Job ID 14340), RREP Cybersecurity Cluster, CAICC, 2026

• Member (UTSA Representative), Quantum Computing Steering Committee (QCSC), ASU Quantum Collaborative, 2023–present

• Member, Faculty Research Immersive Program between UTSA and Tec de Monterrey, June 2025

• Reviewer, FY27 Connecting through Research Partnerships (CONNECT) Program — Artificial Intelligence / Data Science / Analytics area, UTSA–Southwest Research Institute, 2026

College Level (CAICC and KCEID)

• Member (nominated), CAICC Faculty Forum Steering Committee (inaugural), June 2026–present

• Member, CAICC Bylaws Committee (inaugural), February 2025–present

• Member, CAICC MS-AI Standing Committee (inaugural), February 2026–present

• Member, CAICC MS-AI Program Planning Committee (inaugural), September 2025–present

• Member, CAICC BS-AI Program Planning Committee (inaugural), September 2025–present

• Member, CAICC AI Education Initiative Committee (inaugural), May 2026–present

• Member, CAICC Faculty Champions Committee (inaugural), September 2025–present

Department Level (CE and ECE)

• Lead, AI Systems Research Thrust, Department of Computer Engineering, UTSA, 2025–present

• Chair, Signal Processing and Learning Concentration, Department of Electrical and Computer Engineering, 2023–2025

• Member, Department Faculty Review Advisory Committee (DFRAC), Department of Computer Engineering, 2025–present

• Member, DFRAC, Department of Electrical and Computer Engineering, 2022–2025 (reviewed 1 promotion & tenure case and 3 third-year reviews)

• Member, Department Annual Evaluation Policy Task Force, Department of Electrical and Computer Engineering, 2023–2025

• Member, Undergraduate Program Committee, Department of Computer Engineering, September 2025–present

• Member, Graduate Program Committee, Department of Computer Engineering, September 2025–present

• Lead, Development and Design of the Multimodal Sensing and Signal Processing Teaching Lab (approved), UTSA, 2023

• Member, Hiring Committee for AI Accelerators and Bioinformatics Faculty Search (initial search and rerun), Department of Electrical and Computer Engineering / KCEID, 2022–2023

Academic Service at Rochester Institute of Technology

University Level

• Academic Senator, RIT Faculty Senate, 2021–2022

• Core Faculty, Center for Human-Aware AI, Nov. 2019–2022 (Director: A. Savakis)

• Session Chair, Graduate Research Symposium, 2016

• Panelist, Intellectual Property & Technology Transfer Office, Spring 2017

College Level

• Member, Ph.D. Qualification Exam Committee, KGCOE, 2017–2022 (Director: A. Kwasinski)

Department Level

• Chair, Branding Committee, Dept. of Electrical and Microelectronic Engineering, 2021–2022 (Chair: S. Dianat)

Service at Federal Agencies

• Proposal Evaluation Panelist, NSF Directorate for CISE, 2021

• Proposal Evaluation Panelist, NSF Graduate Research Fellowship Program, 2019

• Proposal Evaluation Panelist, NSF Graduate Research Fellowship Program, 2018

• Proposal Evaluation Panelist, NSF Directorate for CISE, 2016

• Academic Research Proposal Reviewer, Army Research Office (ARO), 2021

Leadership and Editorial Roles

• Member, IEEE Signal Processing Society Education Board, 2023–2024

• Chair, Content Production Committee, IEEE Signal Processing Society, 2023–2024

• Associate Editor, IEEE Trans. Artif. Intell., 2022–present

• Associate Editor, IEEE Inside Signal Processing (e-newsletter), 2022–2024

• Editor, IEEE Wireless Commun. Lett., 2017–2019

Conference Organization

• Conference Co-Organizer and Multiple Session Chair, Machine Learning from Challenging Data 2026, SPIE Defense and Security (DS), National Harbor, MD, Apr. 2026

• Conference Co-Founder, Co-Organizer, and Multiple Session Chair, Machine Learning from Challenging Data 2025, SPIE DCS, Orlando, FL, Apr. 2025

• Conference Co-Organizer and Multiple Session Chair, Big Data: Learning, Analytics, and Applications VI, SPIE DCS, National Harbor, MD, Apr. 2024

• Conference Co-Organizer and Multiple Session Chair, Big Data V, SPIE DCS, Orlando, FL, Apr. 2023

• Workshop Co-Organizer, Robust Subspace Learning in Computer Vision (RSL-CV), ICCV 2021, remote, Oct. 2021

• Conference Co-Organizer and Multiple Session Chair, Big Data IV, SPIE DCS, Orlando, FL, Apr. 2022

• Conference Co-Organizer and Multiple Session Chair, Big Data III, SPIE DCS, Orlando, FL, Apr. 2021

• Special Session Co-Organizer and Chair, Tensor Methods for Signal, Data, and Network Analytics, IEEE Asilomar Conf., Pacific Grove, CA, Nov. 2020

• Conference Co-Organizer and Multiple Session Chair, Big Data II, SPIE DCS, Anaheim, CA, Apr. 2020

• Conference Co-Organizer and Multiple Session Chair, Big Data I, SPIE DCS, Baltimore, MD, Apr. 2019

• Co-Organizer and Technical Program Chair, IEEE MLSP, Pittsburgh, PA, Oct. 2019

• Special Session Co-Organizer and Co-Chair, TensorSymp 2, IEEE GlobalSIP, Ottawa, Canada, Oct. 2019

• Workshop Co-Organizer and Chair, 3rd IEEE WCNEE, IEEE INFOCOM 2019, Paris, France, May 2019

• Co-Organizer, Special Session on Signal Processing for Smart City Applications and IoT, IEEE ICASSP, Brighton, UK, May 2019

• Co-Organizer, TensorSymp: Tensor Methods for Signal Processing and Machine Learning, IEEE GlobalSIP, Anaheim, CA, Nov. 2018

• Special Session Co-Organizer and Chair, Signal Processing and Communications for Resilient Autonomous Systems, IEEE SAM, Sheffield, UK, July 2018

• Special Session Co-Organizer and Chair, L1-norm Array Data Processing, IEEE Asilomar, Pacific Grove, CA, Oct. 2018

• Special Session Co-Organizer and Chair, Data Analysis and Learning with Faulty Measurements, SPIE DCS, Orlando, FL, Apr. 2018

• Co-Organizer, 2nd IEEE WCNEE, IEEE INFOCOM 2018, Honolulu, HI, Apr. 2018

• Special Session Co-Organizer and Chair, Advances in Processing Faulty High-Dimensional Data, IEEE CAMSAP, Curacao, Dutch Antilles, Dec. 2017

• Workshop Co-Organizer, 1st IEEE WCNEE, IEEE INFOCOM 2017, Atlanta, GA, May 2017

• Special Session Co-Organizer and Chair, Data/Signal Processing with Faulty Measurements, SPIE DCS, Anaheim, CA, Apr. 2017

Conference Technical Program Committees

• IEEE International Symposium on Wireless Communication Systems (ISWCS), Rio de Janeiro, Brazil, Jul. 2024.

• SwarmNet, 2023

• IEEE SwarmNet 2020 / IEEE WOWMOM 2020, virtual, Aug. 2020

• IEEE SwarmNet 2019 / IEEE WOWMOM 2019, Washington, DC, June 2019

• IEEE MLSP 2018, Aalborg, Denmark, Sept. 2018

• EUSIPCO 2018, Rome, Italy, Sept. 2018

• IEEE MLSP 2017, Tokyo, Japan, Sept. 2017

• EUSIPCO 2017, Kos, Greece, Sept. 2017

Paper Reviews

• Regular reviews for journals: IEEE Trans. Signal Process.; IEEE J. Sel. Topics Signal Process.; IEEE Signal Process. Lett.; IEEE Trans. Image Process.; IEEE Access; EURASIP J. Adv. Signal Process.; IEEE Trans. Commun.; IEEE Trans. Multimed.; IEEE Trans. Wireless Commun.; IEEE Wireless Commun. Lett. [Received Exemplary Reviewer Award, 2017]; IEEE Geosci. Remote Sens. Lett.; INFORMS J. Comput.; J. Franklin Inst.

• Additional reviews for IEEE, SPIE, and EURASIP conferences.

Outreach

• Co-Organizer and Host, NSF AI Spring School, San Antonio, TX, Feb. 2025

• Co-Organizer, NSF ExpandAI Leadership Workshop (EXAIL), Pittsburgh, PA, Oct. 2024

• Co-Organizer, NSF AI Spring School, San Antonio, TX, Mar. 2024

• Mentor, High School Student Research Experience, UT San Antonio

• Participant, Beyond 9.8 Initiative, RIT, 2017 (K-12 mini-courses in engineering for 5th–6th graders)

• Collaborator, RIT Center “Engineers of Color Creating Opportunities (ECCO),” 2017–present

• Chair, Organizing Committee, E3 Engineering and Technology Fair, RIT, 2019