Ken Moon

About

Ken Moon is an Associate Professor of Operations, Technology and Information Management at the Cornell SC Johnson College of Business. He uses large datasets to study and optimize the organization of workers and of markets. His work applies mathematical modeling, causal analysis, AI, and algorithms to improve the performance and treatment of workforces, the operations of online markets, and the design of policies and regulations for complex networks.

Before joining Cornell in 2025, Moon served on the faculty of the Wharton School at the University of Pennsylvania. He received his Ph.D. from the Stanford Graduate School of Business, a J.D. from Harvard Law School, and undergraduate degrees in economics and mathematics from Stanford University.

Moon's research, published in Management Science, has measured how worker turnover disrupts manufacturing productivity and product reliability, how gig-economy workers strategically work across multiple platforms, and how online marketplaces can better match buyers, sellers, and workers. His collaborations span organizations including Apple, Penn Medicine, JD.com, eBay, Meta, and Upwork, and his research has been recognized with multiple best-paper awards from the INFORMS, MSOM, and POMS professional societies.

Moon's current research examines AI in work, teaching, and marketplaces, including whether AI can replace human judgment in marketplace operations, a randomized controlled trial of teaching with AI in the business curriculum, and new work on how AI shapes consumer behavior in online marketplaces. He also develops empirical studies and algorithms that use biometric wearable sensors to anticipate and reduce burnout and fatigue in critical workforces such as ICU nurses. At Cornell, he chaired the committee designing the college's new AI & Business Analytics concentration and served on the organizing committee for the 2026 Cornell Academic-Industry Workshop on the Business of AI. He serves on the Johnson School's AI Curriculum and Research Initiatives Advisory Group (AI-JAG), which he will chair in spring 2027.

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