About
I am a Ph.D. student at the School of Computer Science, The University of Auckland, and a member of the Civilised Agent Lab, under the supervision of Jiamou Liu and Ni Ding.
Previously, I completed my Master's degree in Computer Technology at the University of Electronic Science and Technology of China (UESTC) in 2025, supervised by Prof. Zheng Chang. I also earned my Bachelor's degree from UESTC in 2022.
My research primarily focuses on two areas: analyzing and optimizing interactions in multi-agent AI systems, and designing effective mechanisms for the AI marketplace. I am also interested in mobile computing, wireless communications, and networking.
For a full academic record, please see my CV.
News
Publications
View All →Mechanism Design for a Sustainable Federated Session Recommender System
Runchen Xu, Mengxiao Zhang, Jiamou Liu
ECML PKDD 2026
A mechanism-design approach for sustainable federated session-based recommender systems.
Contract-based Incentive Mechanism for AI-Generated Content Services in Vehicle Edge Computing
Runchen Xu, Lu Yu, Zheng Chang
Conference Proceedings 2025
A contract-theoretic incentive mechanism for AI-generated content services in vehicle edge computing.
Energy-Efficient Joint Optimization of Sensing and Computation in MEC-assisted IoT Using Mean-Field Game
Runchen Xu, Zheng Chang, Zhu Han, Sahil Garg, Georges Kaddoum, Joel J. P. C. Rodrigues
IEEE Internet of Things Journal 2024
Joint optimization of sensing and computation in MEC-assisted IoT systems using mean-field game theory.
Blockchain-Based Resource Trading in Multi-UAV Edge Computing System
Runchen Xu, Zheng Chang, Xinran Zhang, Timo H"am"al"ainen
IEEE Internet of Things Journal 2024
A blockchain-enabled resource trading framework for multi-UAV edge computing systems.
Contract-Based Incentive Mechanism for Blockchain-Enabled Federated Learning in Vehicle Edge Computing
Runchen Xu, Zheng Chang, Zhiwei Zhao, Geyong Min
IEEE Global Communications Conference 2023
A contract-based incentive framework for blockchain-enabled federated learning in vehicle edge computing.