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 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)
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.
One-Sentence Summary: We derive accurate predictions for patients' waiting times in a queue, when the system predictor only observes partial queue length.
One-Sentence Summary: We apply queueing theory and machine learning (ML) to derive interpretable prediction rules for ICU capacity issues in short term.
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.
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
Mathematics Colloquium, William & Mary, 2022