AIMOR @ Banff

Pengyi Shi is an Associate Professor at the Daniels School of Business, Purdue University. She received her Ph.D. degree in Industrial Engineering from Georgia Institute of Technology before joining Purdue in 2014. Her research interests include data-driven modeling and decision-making in healthcare operations. She has collaborated with practitioners from different healthcare organizations worldwide. Most recently, she is collaborating with community correction programs to develop data-based evaluation and human-in-the-loop machine learning algorithms. Her research has won the first place of IISE Outstanding Innovation in Service Engineering Award in 2023, the first place of MSOM Responsible Research in OM Award in 2021, the first place of Pierskalla Best Paper Award in 2018, and a finalist of the INFORMS Daniel H. Wagner Prize in 2023. 

Bridging AI and OM: Enhancing Decision-Making Through Machine Learning

This tutorial explores the integration of AI and ML into OM research. We will look at how state-of-the-art tools such as generative AI, reinforcement learning, and online learning algorithms can help to address operational challenges, such as workforce scheduling, resource allocation, and personalized intervention in dynamic and high-stakes environments. By bridging the gap between theory and practice, this tutorial aims to equip participants with actionable insights and tools to enhance the impact of OM research in industries such as healthcare, public sector, and service operations.

Tinglong Dai is the Bernard T. Ferrari Professor of Business at Johns Hopkins University, where he co-directs the Bloomberg Distinguished Professor Cluster on Global Advances in Medical Artificial Intelligence. He is an expert in human-AI interaction, healthcare analytics, and global supply chains. His current work focuses on integrating AI into clinical workflows to improve physician productivity, patient access, and population outcomes.

A member of the Johns Hopkins faculty since 2013, Dr. Dai is a dynamic and acclaimed MBA teacher. He has developed and taught a first-of-its-kind MBA course, Data Science: Artificial Intelligence, which has been required for all full-time MBA students at Hopkins since 2021. Poets & Quants named him one of the World's Top 40-Under-40 Business School Professors.

A highly sought-after expert, Dr. Dai is frequently featured in major media outlets such as Bloomberg, CNN, The Guardian, The New York Times, NPR, The Wall Street Journal, and The Washington Post. He has appeared on national and international radio and television, including BBC, CNBC, and PBS. In recognition of his thought leadership, Dr. Dai was invited to deliver the commencement address at Carnegie Mellon University's Tepper School of Business Doctoral Commencement and Hooding Ceremony in 2023, as well as at Johns Hopkins University's Full-Time MBA Graduation Ceremony in 2021. He has also served on and spoken at several National Academies of Sciences, Engineering, and Medicine (NASEM) workshops.

In 2023, Dr. Dai was elected as Vice President of Marketing, Communication, and Outreach at INFORMS, the world’s largest association for the decision and data sciences. He serves as an Associate Editor of Management Science, Manufacturing & Service Operations Management, npj Digital Medicine, Health Care Management Science, Service Science, and Naval Research Logistics, and a Senior Editor of Production and Operations Management. 

Dr. Dai has published extensively in top management journals, including Management Science, Manufacturing & Service Operations Management, Marketing Science, and Operations Research. His work has been recognized by numerous awards, including INFORMS Public Sector Operations Research Best Paper Award, POMS Best Healthcare Paper Award, and Wickham Skinner Early Career Award (runner-up). He is a three-time recipient of the Johns Hopkins Discovery Award.

Dr. Dai received his Ph.D. in Operations Management and Robotics in 2013, jointly offered by the Tepper School of Business and the School of Computer Sciences at Carnegie Mellon University.

AI-Inspired Operations: How AI Is Shaping the Field of OM and How OM Can Shape AI

The rapid advancement of artificial intelligence (AI) is driving profound change in the field of operations management (OM). While much attention has been paid to how AI can shape OM practices, the reverse perspective—how OM principles can enhance AI development, deployment, and scaling—remains little explored. This talk highlights the unique opportunity for the OM community to address this gap and produce high-quality research with broader societal impact.

OM, with its systemic perspective and analytical rigor, is uniquely positioned to address critical challenges across the AI lifecycle. OM methods can improve AI training processes, generate real-world evidence of AI’s impact on productivity, efficiency, and sustainability, and balance trade-offs such as model accuracy versus pattern recognition (including those beyond human capabilities). In addition, OM can play a critical role in scaling the AI marketplace, fostering continuous learning, and ensuring alignment between AI developers and stakeholders to enhance safety and accountability.

Using examples primarily from healthcare, as well as insights from other industries, this talk will illustrate how OM can bring order to the world of AI. Time permitting, breakout sessions will encourage brainstorming of potential research topics and collaborations.