Committees

Dai is the Bernard T. Ferrari Professor at the Johns Hopkins Carey Business School. He also holds faculty appointments at the Johns Hopkins Data Science and AI Institute and the School of Nursing. He is a member and faculty chair of the Johns Hopkins University Council and serves on the leadership team of the Hopkins Business of Health Initiative. He co-leads the Bloomberg Distinguished Professorship Cluster on Medical Artificial Intelligence.

A renowned expert in AI, supply chains, and healthcare, Professor Dai has been quoted hundreds of times in the media, including CNN, New York Times, NPR, Wall Street Journal, and Washington Post, and has appeared on BBC News, CNBC, and PBS. In 2021, Poets & Quants named him one of the World’s Best 40 Under 40 Business School Professors.

His research spans healthcare operations, human–AI interaction, global supply chains, and the marketing–operations interface. His work has been published in leading journals such as Management Science, M&SOM, Marketing Science, Operations Research, Journal of Marketing Research, NEJM AI, and npj Digital Medicine.

He serves as Associate Editor for Management Science, M&SOM, npj Digital Medicine, Service Science , Health Care Management Science, and Naval Research Logistics; sits on the Editorial Board of Marketing Science; and is Senior Editor for the INFORMS Journal on Data Science and Production and Operations Management. In 2023, he was elected Vice President of Marketing, Communications, and Outreach for INFORMS. He co-edited the Handbook of Healthcare Analytics (Wiley, 2018), and currently co-eds AI in Supply Chains: Perspectives from Global Thought Leaders (Springer, 2025). He joined Carey in 2013 after earning a PhD in Operations Management and Robotics from Carnegie Mellon University.

Keynote

If Machines Exceed Us: Operations Research and Analytics at an Inflection Point

Artificial intelligence is changing not only the tools of operations research and analytics, but the nature of the problems we take to be solvable. Generative AI, for all its power, does not come with an internal representation of rules, constraints, or risk. Operations research and analytics supply what is missing: the discipline that turns intelligence into decision.

In this talk, I argue that as AI becomes more autonomous, our field becomes more—not less—essential. OR and analytics provide explicit objectives, enforceable constraints, and verifiable guarantees that generative models alone cannot provide. I will also discuss how rising autonomy shifts our work from optimizing decisions to designing decision-making systems, coordinating increasingly agentic AI systems, and contributing to the safety of advanced AI systems, including the long-run challenge of artificial superintelligence.