Weiran Shen

Weiran Shen is a Tenure-Track Associate Professor at the Gaoling School of Artificial Intelligence, Renmin University of China. Prior to this, he was a postdoctoral researcher at Carnegie Mellon University's ISR, working with Fei Fang. He holds a Ph.D. in Computer Science from Tsinghua University's IIIS (advised by Pingzhong Tang) and a B.E. in Electronic Engineering from Tsinghua University.

His research focuses on the intersection of economics and computation, emphasizing auction and mechanism design, game theory, multi-agent systems, and machine learning. Notable work includes studies on security games, dynamic pricing in ad auctions, and fairness in recommendation systems.

Recent publications highlight advancements in Stackelberg/Nash game theory applications, privacy-aware ad auction mechanisms, and resource allocation policies using LLM-based agents. His work bridges theoretical foundations with practical systems, addressing challenges in market design, strategic interactions, and algorithmic fairness.

Shen's research is characterized by interdisciplinary collaboration, spanning computer science, economics, and security domains. He has contributed to both foundational theory (e.g., security games with informants) and applied systems (e.g., coupon design in ad markets).

His academic journey includes significant contributions to conferences like IJCAI, AAAI, and NeurIPS, as well as journals like Artificial Intelligence and Theoretical Computer Science. Current efforts emphasize long-term fairness in recommendation systems and decentralized machine learning frameworks.