Syed Ali Hamza, PhD
- Associate Professor
- Engineering
- Technology
Affiliated Programs
- Electrical Engineering (BS)
- Robotics Engineering (BS)
- Electrical Engineering (MSE)
- Robotics Engineering (MSE)
Education
- PhD, Electrical Engineering (2020)
Villanova University (PA)
About Me
I am passionate about creating an engaging learning environment where students are encouraged to explore, think critically, and develop innovative engineering solutions through hands-on experiences. My research focuses on radar signal processing, sparse and reconfigurable arrays, adaptive beamforming, and artificial intelligence for next-generation sensing systems, with applications ranging from helping self driving vehicles sense their surroundings to radar-based human activity recognition. My work has been supported by the National Science Foundation and industry collaborators, and I actively involve students in research at Widener University to bridge theory with real-world engineering applications.
Research Interests
- Machine Learning and Artificial Intelligence for Sensing and Communications
- High-Resolution Imaging Radar for Self-Driving Vehicles
- RF Sensing for Assisted Living and Remote Patient Monitoring
- Radar-Based American Sign Language (ASL) Recognition and Human Activity Sensing
- Cognitive Radar and Intelligent Sensing Systems
- Massive MIMO and Intelligent Reflecting Surfaces (IRS) for 6G and Beyond
- Adaptive Beamforming and Array Processing
- Statistical Signal Processing
- Sparse Arrays, Sparse Sampling, and Compressive Sensing
- Target Detection, Localization, and Tracking
- Convex Optimization for Signal Processing
Media Expertise
- Radar Sensing and Artificial Intelligence for Self-Driving Vehicles
- Radar-Based Target Detection, Localization, and Tracking
- Signal Processing and Artificial Intelligence for Assisted Living and Remote Patient Monitoring
- Spectrum Sharing and Coexistence of Radar and Communication Systems
- Cognitive Radio, Dynamic Spectrum Access, and Spectral Sensing
Publications
- S. A. Hamza and M. G. Amin, “Hybrid Sparse Array Beamforming Design for General Rank Signal Models,” in IEEE Transactions on Signal Processing, vol. 67, no. 24, pp. 6215-6226, 15 Dec.15, 2019.
- S. A. Hamza and M. Amin, “Sparse Array Design for Maximizing the Signal-to-Interferenceplus-Noise-Ratio by Matrix Completion,” in Digital Signal Processing, Volume 105, 2020, 102678, ISSN 1051-2004.
- S. A. Hamza and M. G. Amin, "Sparse Array Beamforming Design for Wideband Signal Models," in IEEE Transactions on Aerospace and Electronic Systems, vol. 57, no. 2, pp. 1211- 1226, April 2021.
Awards
- National Science Foundation Engineering Research Initiation (ERI) Award, 2024–2026 ($200,000)
- National Science Foundation I-Corps National Teams Award ($50,000), 2026-2027
News
In the Media
Noteworthy
Engineering Professor Awarded Nearly $200,000 from National Science Foundation
Ali Hamza, assistant professor of electrical engineering, has been awarded approximately $200,000 in grant funding from the prestigious Engineering Research Initiation program from the National Science Foundation, or NSF. The grant will support Hamza's research, which aims to revolutionize cognitive sensing technologies for radar and wireless communication systems. By enhancing interference mitigation, using artificial intelligence techniques, Hamza’s pioneering work promises improved signal detection with applications spanning wireless communication, aerospace, healthcare, and automotive industries. These contributions will significantly advance the radar imaging for self-driving cars, weather and military radar, radar-based human activity monitoring, fall detection, and remote vital sign estimation. With the integration of machine learning and AI algorithms, the project seeks to optimize radio frequency spectrum utilization, alleviate congestion, and expand bandwidth, ultimately enhancing quality of service and regulatory capabilities.
The funding, which marks the first NSF grant for the electrical engineering department, will support undergraduate and graduate research.