About Me
Jinwu Xiao is a Ph.D. student in the School of Construction Management Technology at Purdue University, where he serves as a Research Assistant in the SIMPLE Lab under Prof. Kyubyung Kang. He is concurrently pursuing an M.S. in Computer and Information Technology. His research focuses on computer vision, 3D reconstruction, LiDAR point cloud processing, automation, and digital twins for construction and infrastructure management.
Research Interests
- Computer vision applications in construction and infrastructure: Exploring how computer vision can enhance monitoring and management.
- Automation in construction and infrastructure management: Developing systems to improve efficiency and safety.
- Digital twin technology for enhanced asset monitoring and maintenance: Using digital twins to better understand and maintain infrastructure assets.
- Deep learning and machine learning techniques for data processing and analysis: Applying cutting-edge AI techniques to process and analyze construction data.
- LiDAR scanning and point cloud data processing: Utilizing LiDAR technology for accurate mapping and modeling of construction sites.
Research Experience
- Developing a framework for integrating AI-based LLM reasoning into BIM workflows for design automation
- Enhancing construction site digital twins with language embedded radiance fields
- Developing deep learning-based 3D point cloud semantic segmentation for MEP systems
- Establishing benchmarks for cost-effective model training methods using data from related domains and YOLO for automated inspection systems
- Examining the viability of 3D scanning technology for bridge inspection and proposing frameworks to evaluate the residual strength of steel girder members
- Evaluating the effectiveness of deep learning techniques, such as RandLA-Net, in denoising LiDAR data compared to traditional and other machine learning-based methods
- Proposing reasoning frameworks that integrate Large Language Models (LLMs) to extend the hierarchical structure of BIM models in accordance with Industry Foundation Classes (IFC) standards
- Developing deep learning models for hydrant segmentation and extraction in large-scale urban point clouds
Contact
- Email: xiao270@purdue.edu
- LinkedIn: Jinwu Xiao
- Lab: Purdue SIMPLE Lab