Kuang Xu (Chinese: 许匡) is a Tenured Associate Professor at Stanford Graduate School of Business and, by courtesy, an Associate Professor of Electrical Engineering at Stanford University. His research group focuses on developing advanced AI and decision systems for physical reality, spanning marketplaces, logistics, healthcare, advanced manufacturing, and scientific discovery.
Professor Xu was a Senior Staff Scientist at Uber, where he led the creation and deployment of one of Uber’s first consumer agentic AI systems. His industry roles also include Chief AI Advisor to Shipt, a Target subsidiary, and advisor to multiple AI and deep-tech startups.
Professor Xu’s research has appeared in leading journals including Operations Research, Management Science, and the American Economic Review, and he has served as an Associate Editor for both Management Science and Operations Research. He launched AI Strategy, Stanford’s first course on building and leading AI-native products and organizations. His honors include the INFORMS Nicholson Prize, the ACM SIGMETRICS Best Paper Award, and the ACM SIGMETRICS Rising Star Research Award.
Professor Xu received his Ph.D. in EECS from MIT and his B.S. in EE from the University of Illinois at Urbana-Champaign. Professor Xu is a native of Suzhou, China.
Recent work
All highlights
Agentic Laboratories of the Future
What Is the Long-Term Value of Reliability?
Uber Consumer AI
Experimenting under Stochastic Congestion
Non-Stationary Bandit Learning via Predictive Sampling
Behavioral Generative Agents for Energy Operations
Treatment Effects in Market Equilibrium
Load Balancing Using Sparse Communication
Weak Signal Asymptotics for Sequentially Randomized Experiments
