The University of Texas at San Antonio

Machine Learning Optimization Laboratory

Welcome to the Machine Learning Optimization (MILO) Laboratory at The University of Texas at San Antonio (UTSA).

Our team conducts cutting-edge research in machine learning and signal processing, with a focus on developing optimized, robust, computationally efficient, and explainable methods for learning from complex data.

Current research areas include robust learning, dynamic/continual learning, distributed/federated learning, few-shot learning, multimodal learning, and quantum machine learning.

Our work is grounded in theoretical foundations and addresses challenging real-world problems in various application areas, primarily including wireless communications, remote sensing, and healthcare.

Contact Information

Director: Dr. Panagiotis (Panos P.) Markopoulos
E-mail: panagiotis.markopoulos@utsa.edu; panos@utsa.edu
Address: Rooms 340E and 340H, San Pedro 1 Building, 506 Dolorosa St, San Antonio, TX 78204

MILO Team, September 2023.

Recently Funded Projects

Title: PARTNER: Neuro-Inspired AI for the Edge at UTSA (NAIAD). Funding agency: National Science Foundation. Award period: September 2023 - August 2026. Total obliged amount: $2,800,000. Role: Co-PI (PI: Dr. Dhireesha Kudithipudi).
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.