Machine Learning Optimization & Signal Processing Laboratory
The University of Texas at San Antonio
Multiple research openings available for undergraduate and graduate students.
Welcome to the Machine Learning Optimization & Signal Processing (MELOS) Lab's webpage!
We are a team of researchers at The University of Texas at San Antonio (UTSA), lead by Prof. Panos Markopoulos, working on challenging and impactful problems in the areas of machine learning, data analysis, and signal processing. Our mission is to advance efficient, explainable, and trustworthy artificial intelligence. Our focus is on both fundamental research (theory and algorithms), as well as practical solutions in a wide range of applications, including computer vision, remote sensing, wireless communications, and healthcare. MELOS is a core lab of the UTSA School of Data Science, housed in state-of-the-art facilities at the new San Pedro 1 building.
Contact
Address: Room 340E, San Pedro I Building, 506 Dolorosa St, San Antonio, TX 78204
Connect: LinkedIn, Google Scholar, ORCiD, GitHub
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.
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.
© Copyright 2023 Panagiotis Markopoulos