Siyi Li

Papers

Total Citations

H-Index

About

Siyi Li is a researcher whose work spans the intersection of computer vision, robotics, and neural engineering, with a particular focus on developing intelligent systems that bridge perception and human performance. Li's most recognized contribution, the High-Dimensional Regression Network (HDRNet), addresses the fundamental challenge of 3D point cloud registration — a critical process in reverse engineering, computer vision, and robotic navigation. By leveraging deep learning to estimate transformation matrices that align source and target point clouds, this 2022 work has garnered 7 citations and represents a meaningful advance in learning-based geometric processing. Beyond spatial computing, Li has also contributed to the field of rehabilitation engineering, exploring online assessment methods for neural engagement and functional performance during fine motor control tasks. This work, published in 2018, reflects a commitment to translating computational intelligence into clinically meaningful applications, particularly for improving robot-assisted rehabilitation outcomes by addressing patient motivation and engagement. Together, these contributions reveal a researcher who operates at a productive crossroads of machine learning, 3D sensing, and human-machine interaction — demonstrating both technical rigor and a drive to solve real-world problems in health and robotics.