Hamza Ahmed Abushahla
I'm a Master's student in Machine Learning at the American University of Sharjah, where I also earned my B.Sc. in Computer Engineering and a minor in Engineering Management.
I currently work as a research and teaching assistant in the department of Computer Science and Engineering. My research focuses mainly on model compression, optimization and quantization for deploying neural networks on resource-constrained edge devices. Additionally, I explore energy-efficient computing and intelligent autonomous robotics.
Publications
These are papers that have already been published.
Cognitive Radio Spectrum Sensing on the Edge: A Quantization-Aware Deep Learning Approach
IEEE Communications Letters, 2025
We study the effect of quantization-aware-training (QAT) on two SOTA spectrum sensing models - DeepSense and ParallelCNN. Models are deployed on a Sony Spresense for hardware evaluation.
Real-Time Student Engagement Monitoring on Edge Devices: Deep Learning Meets Efficiency and Privacy
IEEE Global Engineering Education Conference (EDUCON), 2025
This work explores the deployment of deep learning models on resource-constrained edge devices to monitor student engagement in real time, with an emphasis on efficiency and privacy.
Accepted Papers
These are papers that have been accepted for publication, but are not yet up.
Submitted Papers
These are papers that have been submitted for publication, but have not yet been released.
Research Projects
These include coursework, side projects and unpublished research work.
Navigate Without Forgetting: Continual Reinforcement Learning for Mobile Robot Navigation in Webots
MLR555 Project, 2025
This project implements a Continual Reinforcement Learning (CRL) framework for mobile robot navigation using the e-puck robot in Webots. We design a unified agent trained sequentially across multiple tasks—maze navigation, line following, and obstacle avoidance—using Soft Actor-Critic (SAC). The setup evaluates the agent’s ability to learn new behaviors while retaining previously acquired skills.
From Script to Digital: A Deep Learning Approach to Arabic Handwriting Recognition
MLR503 Project, 2024
This was my MLR503: Data Mining and Knowledge Discovery Course Research Project. We developed an end-to-end deep learning-based handwritten text recognition (HTR) system for Arabic script leveraging the KHATT Dataset. To further enhance recognition accuracy, we incorporated KenLM for post-processing.
Cognitive Radio Spectrum Sensing and Allocation: A Low-Complexity Deep Learning Approach
Senior Design Project, 2024
This was my B.Sc. in Computer Engineering Senior Design Project, focused on spectrum sensing and allocation using a low-complexity deep learning-based (CNN) spectrum sensing algorithm. The project involved developing and quantizing the CNN model, which was deployed on hardware for real-time operation. The solution was demonstrated both in simulation and on hardware, utilizing a Raspberry Pi as the central node, RTL-SDR for signal sensing, and LoRa transceivers for communication. This dual demonstration validated the practicality and efficiency of the approach in addressing dynamic spectrum management challenges.
Miscellanea
Teaching Assistantships
- Software: CMP120 – Programming I, CMP220 – Programming II, CMP305 – Data Structures & Algorithms, CMP321 – Programming Languages
- Hardware: COE425 – Modern Computer Organization
Awards & Honors
- 2nd place at the AUS College of Engineering senior design projects competition
- Full scholarship for undergraduate studies awarded by Ministry of Presidential Affairs, UAE
- Member of the IEEE-Eta Kappu Nu Honors Society