Yuting Yuan

I am a Visiting Assistant Professor of Mathematics and Data Science at the College of William & Mary.

Curriculum Vitae | Email

My research is focused on service operations and interpretable decision making in innovative business models. I am particularly interested in integrating mathematical modeling with data science to derive practical decision support tools that have solid theoretical foundations.

I received my Ph.D. in Operations Management at Simon Business School, University of Rochester, under the supervision of Professor Yaron Shaposhnik.

Research (Reverse chronological order)

The Impact of the Zero-COVID Policy and its Implication on Scheduling Supply Delivery

  • with Colin Tang (undergraduate student); in Progress

One-Sentence Summary: In memory of those who have suffered, click for more information.

Staff Sharing Under Uncertainty

One-Sentence Summary: I propose 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

  • with Yoav Kerner, Ricky Roet-Green, Arik Senderovich and Yaron Shaposhnik (Major revision, M&SOM; latest draft)

One-Sentence Summary: We derive accurate predictions for patients' waiting times in a queue, when the system predictor only observes partial queue length.

Interpretable Rules for Predicting Congestion Risk in Queueing Systems: Applications to ICUs

  • with Fernanda Bravo, Cynthia Rudin and Yaron Shaposhnik (Major revision, Stochastic Systems; latest draft)

One-Sentence Summary: We apply queueing theory and machine learning (ML) to derive interpretable prediction rules for ICU capacity issues in short term.

Research Yuting Yuan.mp4

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

Information Visibility in Omnichannel Queues

  • with Ricky Roet-Green (working draft available at SSRN)

One-Sentence Summary: We 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, 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