Suyu Ma
Papers
1
Total Citations
2
H-Index
1
About
Suyu Ma is a researcher at the forefront of intelligent transportation systems and human-robot interaction, with a focus on real-time safety and efficiency. Their most-cited work, "LiteFat: Lightweight Spatio-Temporal Graph Learning for Real-Time Driver Fatigue Detection" (2025), introduces a novel approach to detecting drowsy driving—a leading cause of traffic accidents. By leveraging lightweight spatio-temporal graph learning, Ma addresses the critical challenge of deploying deep learning models on resource-constrained embedded devices, achieving low-latency, accurate fatigue detection without sacrificing performance. This contribution bridges the gap between advanced AI and practical road safety, offering a scalable solution for autonomous vehicles and driver-assistance systems. With 2 citations to date, Ma’s work is gaining traction for its innovative fusion of graph neural networks and real-time edge computing. Their research underscores a commitment to making AI-driven safety systems both accessible and deployable in real-world robotic and vehicular contexts. Ma’s achievements highlight a promising trajectory in developing efficient, high-impact technologies for human-centered automation.
Research Focus
Key Achievements
Top Papers
- 1