Panagiotis (Panos) Markopoulos, Ph.D.
Margie and Bill Klesse Endowed Associate Professor
Departments of Electrical & Computer Engineering and Computer Science
The University of Texas at San Antonio
Contact Information
Address: One UTSA Circle, San Antonio, TX 78249
E-mail: Panagiotis.Markopoulos@utsa.edu
Website: https://www.markopoulos.org/
Areas of Expertise
Machine learning, signal processing, and data science, with emphasis on theory, algorithms, and practical applications.
Current research topics:
Machine learning from limited and/or corrupted data
Federated learning with privacy constraints
Adaptive and continual learning in dynamic environments
Multimodal learning and data fusion
Quantum machine learning
Application areas include, among others, remote sensing, computer vision, wireless systems, and healthcare.
Brief Biography
I am the Margie and Bill Klesse Endowed Associate Professor with the Departments of Electrical and Computer Engineering and Computer Science at The University of Texas at San Antonio (UTSA). I am also Director of the UTSA Machine Learning Optimization Laboratory and Co-Lead for Trustworthy AI of the MATRIX: The UTSA AI Consortium for Human Well-Being. Prior to joining UTSA, I was a tenured Associate Professor with the Rochester Institute of Technology (RIT). In the Summers of 2018, 2020, and 2021, I was a Visiting Faculty (Independent Contractor) at the U.S. Air Force Research Laboratory (AFRL), Information Directorate, in Rome NY.
My expertise is in the areas of machine learning, artificial intelligence, signal/image processing, data science, and wireless communications. My research mission is to advance efficient and trustworthy solutions in these areas. Together with students and collaborators, I have co-authored more than 80 journal and conference articles and 3 book chapters. My received has been funded 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, I received the Young Investigator Program (YIP) Award, from the AFOSR. In 2021, I was elevated to the grade of IEEE Senior Member.
Professional Positions
Margie and Bill Klesse Endowed Associate Professor
Department of Electrical & Computer Engineering and Department of Computer Science, The University of Texas at San Antonio (UTSA), San Antonio, TX, 8/2022 – Present.
Concurrent Roles:
Founding Director, Machine Learning Optimization Laboratory, 2022 – Present.
Founding Director, Multimodal Sensing and Signal Processing Education Laboratory, 2023 – Present.
Chair, Signal Processing and Learning Concentration, ECE Dept., 2022 – Present.
Co-Lead for Trustworthy AI, MATRIX: The UTSA AI Consortium for Human Well-Being, 2024 – Present.
Core Faculty Member, UTSA School of Data Science, 2022 – Present.
Core Faculty Member, MATRIX: The UTSA AI Consortium for Human Well-Being, 2022 – Present.
Associate Professor (with Tenure)
Department of Electrical and Microelectronic Engineering, Rochester Institute of Technology (RIT), Rochester, NY, 2021 – 8/2022.
Concurrent Roles:
Director, Machine Learning Optimization & Signal Processing (MILOS) Lab
Core Faculty, RIT Center for Human-aware Artificial Intelligence (CHAI)
Extended Faculty, PhD Program in Computing and Information Sciences
Extended Faculty, PhD Program in Mathematical Modeling
Member, RIT Faculty Senate (2021-2022)
Assistant Professor (Tenure-Track)
Department of Electrical and Microelectronic Engineering, Rochester Institute of Technology, Rochester, NY, 2015 – 2022.
Visiting Faculty (Independent Contractor)
Visiting Faculty Research Program, U.S. Air Force Research Laboratory (AFRL), Information Institute, Rome, NY, Summers of 2018, 2020, 2021.
Graduate Research Assistant
Department of Electrical Engineering, The State University of New York at Buffalo (UB), Buffalo, NY, 2011 – 2015.
Education
Ph.D., Electrical Engineering
State University of New York at Buffalo, Buffalo, NY, 2015
Dissertation: “Optimal Algorithms for L1-norm Principal Component Analysis: New Tools for Signal Processing and Machine Learning with Few and/or Faulty Training Data.”
M.S., Electronic and Computer Engineering
Technical University of Crete, Chania, Greece, 2012
Thesis: “Full-rate Differential M-PSK Alamouti Modulation with Polynomial-complexity Maximum-likelihood Noncoherent Detection.”
Engineering Diploma (5-year program), Electronic and Computer Engineering
Technical University of Crete, Chania, Greece, 2010
Thesis: “Maximum-Likelihood Noncoherent M-PSK OSTBC Detection with Polynomial Complexity.”
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