Zijun Zhan

Papers

Total Citations

H-Index

About

Zijun Zhan is a rising researcher at the intersection of artificial intelligence, blockchain technology, and social equity in the construction industry. Their work focuses on leveraging deep learning and decentralized systems to address systemic biases, particularly gender discrimination, within traditionally male-dominated sectors. Zhan’s most-cited paper, “Deep Learning and Blockchain-Driven Contract Theory: Alleviate Gender Bias in Construction” (2024), proposes a novel framework that combines AI-driven worker evaluation with smart contracts to create fairer recruitment processes. This work challenges conventional hiring paradigms by introducing objective, data-driven criteria that prioritize skill and performance over demographic factors. Although early in their career, Zhan’s research has already garnered attention for its innovative fusion of technical rigor and social impact, earning 5 citations within a year of publication. Their approach—bridging contract theory, blockchain transparency, and machine learning—offers a scalable solution to one of the construction industry’s most persistent inequities. Zhan’s contributions are particularly timely as teleoperation and robotics reshape workforce demands, positioning them as a forward-thinking voice in ethical AI deployment and labor reform.