Mr. Lin Li, Chengdu University of Technology, China
Li Lin received his M.S. degree in Educational Technology from Sichuan Normal University in Chengdu, China, in 2011. He is currently pursuing a Ph.D. in Earth Exploration and Information Technology at the same institution. His research interests focus on machine learning theory and 3D point cloud processes. From July 2011 to July 2019, Li Lin worked as a Senior Engineer specializing in production design at ThinkGeo (US) Science and Technology Co., Ltd. in Chengdu, Sichuan. With a strong background in computer science, Li Lin continues to contribute to the fields of technology and research.
Professional Profile:
Summary of Suitability for Best Innovation Award:
Li Lin’s background and research accomplishments demonstrate significant expertise and innovation in the field of 3D point cloud processes, particularly in machine learning and LiDAR technology. His academic journey, with an M.S. degree in educational technology and current Ph.D. studies in Earth exploration and information technology, shows his commitment to advancing technological solutions in a complex and emerging area. His research focuses on applying machine learning theory to 3D point cloud processing, which is crucial for various applications like geospatial analysis and environmental monitoring.
Education:
- Master’s Degree in Educational Technology
- Institution: Sichuan Normal University, Chengdu, China
- Duration: September 1, 2008, to July 1, 2011
- Ph.D. in Earth Exploration and Information Technology (Pursuing)
- Institution: Sichuan Normal University, Chengdu, China
- Current Status: Ongoing
Work Experience:
- Senior Engineer (Production Design)
- Company: ThinkGeo (US) Science and Technology Co., Ltd., Chengdu, Sichuan, China
- Duration: July 1, 2011, to July 3, 2019
Research Interests:
- Machine Learning Theory
- 3D Point Cloud Processes
This outlines Li Lin’s career trajectory and expertise in both education and industry
Publication top Notes:
Compressing and Recovering Short-Range MEMS-Based LiDAR Point Clouds Based on Adaptive Clustered Compressive Sensing and Application to 3D Rock Fragment Surface Point Clouds