Kuang Xu (Chinese: 许匡) is a Tenured Associate Professor at Stanford Graduate School of Business. He is an expert in Operations Research, AI and Data Science innovation, supply chains and logistics, and data-driven decision-making. He is a Co-Creator of AI and Data Science Strategy, the first Stanford course focusing on the strategy, management and entrepreneurship of AI and Data Science. He is a Co-Director of the Stanford GSB Value Chain Innovation Initiative.

Kuang’s research focuses on decision-making under uncertainty, leveraging tools from operations research, statistics and machine learning. His work has been published in leading academic journals including Operations Research and Management Science, and has received a number of prestigious awards, including the George E. Nicholson Prize from the Institute for Operations Research and the Management Sciences (INFORMS), the Best Paper Award as well as Outstanding Student Paper Award from the Association for Computing Machinery (ACM), Special Interest Group on Measurement and Evaluation (SIGMETRICS), and an ACM SIGMETRICS Rising Star Research Award. He serves as an Associate Editor for Management Science and Operations Research in Data Science and Stochastic Modeling. His research and writing have been featured in a variety of media outlets including the NPR, PBS, NBC and USA Today.

Kuang advises companies and investment funds on how to build core AI and Data Science capabilities and strategic moats. He has served as the Chief Data Science Advisor for Shipt Inc., Senior Advisor to Uber Inc., and scientific advisors to a number of startups as well as venture capital and private equity funds. 

Kuang received his Ph.D. degree in Electrical Engineering and Computer Science from MIT (2019), and the Bachelor of Science degree from the University of Illinois at Urbana-Champaign (2009).  He is a native of Suzhou, China.