Jianwei Huang (CUHK)

Jun 8, 2026
Soda Hall 510

Title and Abstract

When Machines Learn to Think Strategically

Strategic behavior—bargaining, competition, and negotiation—is everywhere, yet modeling it remains slow and expert-dependent. This talk argues that large language models enable a fundamental shift: strategy can become a computational object that is scalable, interpretable, and auditable. In this talk we will present two systems. AutoBM automatically discovers interpretable behavioral models from human decision data, achieving a 73% reduction in prediction error in auction settings. PrimeNash automates game-theoretic equilibrium derivation with machine-verifiable proofs, succeeding on both static and dynamic games. Together, they point toward a new standard: strategic AI that is not merely impressive, but understandable and trustworthy.

The School of Science and Engineering at CUHK-Shenzhen warmly welcomes inquiries from researchers interested in joining a vibrant, internationally oriented faculty at the frontier of AI, engineering, and interdisciplinary science. Both postdoc and faculty applications are welcome.

Bio

Jianwei Huang is Presidential Chair Professor, Associate Vice President, and Dean of the School of Science and Engineering at The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen). He also serves as Associate Director of the Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS). His research spans communications, networking, AI, economics, and energy systems. Huang has served as Editor-in-Chief of IEEE Transactions on Network Science and Engineering, Associate Editor-in-Chief of the IEEE Open Journal of the Communications Society, and Chair of the IEEE Koji Kobayashi Award Committee. He is an IEEE Fellow, an IEEE ComSoc Distinguished Lecturer, a Clarivate Web of Science Highly Cited Researcher, and an Elsevier Most Cited Chinese Researcher.