Speakers
Jing Dong
DeRosa Family Associate Professor of Business Decision, Risk, and Operations Division, Graduate School of Business, Columbia University
Jing Dong is the DeRosa Family Associate Professor of Business in the Decision, Risk, and Operations Division at Columbia Business School and a visiting faculty member at the Antai College of Economics and Management at Shanghai Jiao Tong University. Her research lies at the intersection of applied probability and service operations management, with a particular focus on patient flow management in healthcare delivery systems. She serves as an associate editor for Operations Research, Mathematics of Operations Research, Management Science, Manufacturing & Service Operations Management, and the INFORMS Journal on Computing, and as vice chair of the INFORMS Applied Probability Society. She received her Ph.D. in Operations Research from Columbia University and was on the faculty at Northwestern University before joining Columbia Business School.
| Date: |
| Wednesday, 14 October 2026 |
| Time: |
| 10:00 am - 11:30 am |
| Venue: |
|
NUS Business School Mochtar Riady Building BIZ1 0305 15 Kent Ridge Drive Singapore 119245 (Map) |
Abstract
Queueing network models are powerful mathematical modeling tools for managing congestion across various applications, yet broader adoption remains limited due to challenges in accessibility and scalability. This talk explores how recent advances in AI can enable more accessible and practical applications of this modeling tool. We first present a model-based reinforcement learning framework, called differentiable discrete event simulation, to solve general queueing network control problems. Our approach enables efficient and scalable policy gradient evaluation using auto-differentiation software. We then present a new data-driven modeling framework based on autoregressive sequence models. This framework allows us to utilize the rich operational data to automatically learn and represent system dynamics. Together, these methods demonstrate how AI may help enhance the reach and usability of queueing models, helping address operational challenges across diverse domains.