报告题目：AI for Scalable and Practical Queueing Network Modeling and Control

主 讲 人：哥伦比亚大学商学院董婧副教授

时  间：2026年10月23日10：30 -- 12：00

地  点：9教326会议室

报告摘要：

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.

主讲人简介

董婧，哥伦比亚大学商学院副教授，上海交通大学安泰经济与管理学院访问教授，哥伦比亚大学运筹学博士，曾任职于西北大学。研究聚焦应用概率与服务运营管理交叉领域，重点研究医疗服务系统中的患者流量管理。担任《Operations Research》《Mathematics of Operations Research》《Management Science,》《Manufacturing & Service Operations Managemen》以及《 INFORMS Journal on Computing》副主编，同时担任国际运筹学与管理科学学会（INFORMS）应用概率分会副主席。

                  欢迎感兴趣的老师和同学参加！