Yuting Yuan
I am a Visiting Assistant Professor of Mathematics and Data Science at the College of William & Mary.
My research is focused on service operations and interpretable decision-making. I am particularly interested in integrating mathematical modeling with data science to derive practical decision support tools with solid theoretical foundations.
I received a Ph.D. in Operations Management at Simon Business School, University of Rochester, under the supervision of Professor Yaron Shaposhnik.
Selected Research (Reverse chronological order)
Sweeten Chick-fil-A Traffic Jam: Congestion Management using Math Modeling and Data Analytics
Yuting Yuan; Submitted, Companion notebookThis case study utilizes four modules to explore the optimal design of the Chick-fil-A drive-thru, demonstrating a cohesive and practical decision-making pipeline for tackling a well-known congestion problem.
Interpretable Routing in Disaster Response Management
(Previously, "The Impact of the Zero-COVID Policy and its Implication on Scheduling Supply Delivery")
Colin Tang, Yuting Yuan; In Progress, DataStaff Sharing Under Uncertainty
Yuting Yuan; Major revision, M&SOM, DraftI propose a capacity-sharing strategy for service systems with worker shortages, which leads to significant cost-savings compared with a benchmark rule that staffs each unit independently.
Waiting-Time Prediction with Invisible Customers
Yoav Kerner, Ricky Roet-Green, Arik Senderovich, Yaron Shaposhnik, Yuting Yuan; Minor revision, M&SOM, DraftWe derive accurate predictions for patients' waiting times in a queue when the system predictor only observes partial queue length.
Interpretable Prediction Rules for Congestion Risk in Intensive Care Units
Fernanda Bravo, Cynthia Rudin, Yaron Shaposhnik, Yuting Yuan; Stochastic Systems, forthcomingWe apply queueing theory and machine learning (ML) to derive interpretable prediction rules for ICU capacity issues in the short term.
Information Visibility in Omnichannel Queues
Ricky Roet-Green, Yuting Yuan; Draft available at SSRNWe study the impact of the invisible queue in an omnichannel system on throughput and social welfare, where customers strategically decide to join or balk.
Conference Talks
INFORMS Annual Meeting, Phoenix, AZ (Session SB08), Upcoming
INFORMS Annual Meeting, Indianapolis, IN, 2022
INFORMS Healthcare Conference, Virtual, 2021
CORS Annual Conference, Virtual, 2021
POMS 31th Annual Conference, Virtual, 2021
INFORMS Annual Meeting, Virtual, 2020
INFORMS Annual Meeting, Seattle, WA, 2019
INFORMS Workshop on Data Science, Seattle, WA, 2019
INFORMS Healthcare Conference, Cambridge, MA, 2019
POMS 30th Annual Conference, Washington D.C., 2019
Seminar Talks
Center for Advanced Medical Analytics, University of Virginia, 2023
Mathematics Colloquium, William & Mary, 2022
Telfer School of Management, University of Ottawa, Ottawa, Canada

A 5-minute video tour of my research (produced in 2021).