Lei Zhang

Chongqing University, Shanghai Dianji University, Beijing University of Civil Engineering and Architecture

Papers

Total Citations

339

H-Index

About

Lei Zhang is a researcher whose work spans machine learning, robotics, and wireless communication systems. He is perhaps best recognized for his influential contributions to transfer learning, particularly his landmark 2022 survey, "A Survey on Negative Transfer," which has accumulated an impressive 331 citations and stands as a definitive reference in the field. This work critically examines the pitfalls of transfer learning — specifically the phenomenon of negative transfer, where knowledge from source domains actively harms performance in target domains — addressing a challenge of profound importance given the widespread deployment of transfer learning in data-scarce and privacy-sensitive applications. Beyond machine learning theory, Zhang's earlier research demonstrates a broad technical foundation in robotics and embedded systems. His investigations into Zigbee-based indoor localization for mobile robots and networked teleoperation systems reflect hands-on expertise in wireless communication protocols and real-time robot control, showcasing his ability to bridge theoretical frameworks with practical engineering solutions. Zhang's trajectory — from hardware-level robotics research to high-impact machine learning surveys — illustrates a researcher who has evolved with the field, ultimately making his most significant mark in understanding the boundaries and failure modes of transfer learning, a contribution that continues to guide practitioners and theorists alike.