Yen-Yu Wu
Papers
1
Total Citations
3
H-Index
1
About
Yen-Yu Wu is a leading researcher at the forefront of embedded artificial intelligence and industrial automation, with a primary focus on integrating tiny machine learning (TinyML) with edge computing for real-time cyber-physical systems. Wu’s most impactful contribution is the pioneering integration of visual recognition technology and multi-object detection into industrial robotic arms, dramatically enhancing production line flexibility and automation. By fusing TinyML algorithms with edge computing devices, Wu demonstrated how lightweight, low-power models can enable real-time object recognition directly on robotic hardware, eliminating reliance on cloud infrastructure. This work, published in 2025 and already garnering 3 citations, represents a significant step toward scalable, intelligent manufacturing. Wu’s research addresses critical challenges in latency, energy efficiency, and on-device intelligence, positioning TinyML as a practical solution for Industry 4.0. Through this achievement, Wu has established a clear path for deploying machine vision in resource-constrained industrial environments, inspiring further exploration into autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1