Yining Wen
Papers
3
Total Citations
14
H-Index
2
About
Yining Wen is a rising researcher at the forefront of intelligent robotics and built-environment inspection, pioneering the integration of large language models (LLMs) with autonomous systems. Her work centers on developing multimodal sensing and AI-driven navigation for indoor building defect detection, addressing critical challenges in preventative maintenance. Wen’s key contributions include the first frameworks to combine GPT-based natural language understanding with dense captioning and object detection for robotic inspections, enabling UAVs to autonomously identify defects like cracking, spalling, and moisture without complex manual control. Her 2024 paper on autonomous defect detection using multimodal learning and GPT has already garnered 7 citations, while her work on natural language navigation for robotic systems has attracted 5 citations, reflecting the field’s rapid interest. Wen’s research notably reduces reliance on labor-intensive, error-prone manual inspections, offering scalable solutions for building system longevity. Her innovative sensing system, detailed in a 2024 publication, further demonstrates how mobile robots can autonomously assess structural health, marking a significant step toward fully automated facility management. As a young scholar, Wen’s work is shaping the next generation of intelligent, adaptive inspection technologies.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3