My research is focused on service operation and interpretable decision making in innovative business models. I am primarily interested in utilizing data science to derive decision support tools for complex service systems.
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 the commonly used independent staffing rule.
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.
Operations Management, MBA (Rating: 4.5/5)
Advanced Business Modeling, MBA/MS
Programming for Analytics, MS
Social Media Analytics, MS
Information System for Management, MBA
Introduction to Business Analytics, MBA/MS
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