Yunfeng Zhang

Yunfeng Zhang is a Professor at Shandong University of Finance and Economics specializing in Artificial Intelligence with significant contributions to Explainable AI, Human-Computer Interaction, and Machine Learning. His research spans computer vision, fairness in AI, and robotics applications across diverse domains including transportation, agriculture, and cybersecurity. Zhang has established strong international collaborations with IBM Research (notably with Kush Varshney and Q. Vera Liao) and publishes extensively in top venues like CHI, KDD, and IEEE Transactions.

His research interests focus on making AI systems transparent and trustworthy through the AI Explainability 360 toolkit development since 2020, human-AI collaboration frameworks, and fairness optimization in machine learning. Recent work shows a strategic pivot toward applied AI problems including urban rail transit optimization, agricultural robotics for citrus detection, and VR-enhanced education systems in China. Zhang's publications demonstrate exceptional breadth across both theoretical foundations and real-world implementations of AI technologies.

Zhang's recent publications (2023-2025) reveal strong trends in multimodal AI applications, with significant work in computer vision for remote sensing and industrial automation, reinforcement learning for transportation systems, and educational technology innovations. His work consistently bridges technical AI development with human-centered design principles, particularly evident in his CHI publications on human-AI collaboration interfaces.

• Co-developer of the influential AI Explainability 360 open-source toolkit (JMLR 2020)
• Key contributor to Uncertainty Quantification 360 framework
• Active participant in ACM FAccT conferences on AI fairness
• Principal investigator on multiple Chinese national research grants for AI applications

Zhang leads research teams developing practical AI solutions for Chinese industry challenges, particularly in transportation optimization and agricultural technology. His lab maintains strong industry partnerships with Chinese railway authorities and agricultural technology firms, focusing on implementing AI systems that address specific local infrastructure and productivity challenges while maintaining rigorous academic standards.