LabCompass

Pengyi Shi

Health Professions · Purdue University West Lafayette

Mid career · publishing since 2010

Publications

63

Citations

923

Est. group size

~1

Recurring co-author estimate

Active years

17

Publishing since 2010

Research summary
AI-generated

Pengyi Shi's research focuses on improving how hospitals and other public-service systems (such as criminal justice and community corrections) manage capacity, scheduling, and patient/case flow using mathematical modeling, queueing theory, and machine learning. Much of the work combines operations research methods (like optimization and reinforcement learning) with predictive modeling to support decisions such as patient admission, hospital bed allocation, and case routing. The research often bridges healthcare operations with data-driven decision-making tools that could apply to other service systems facing similar capacity or fairness challenges.

Healthcare operations and capacity managementQueueing theory and stochastic modelingMachine learning for predictive healthcare modelsReinforcement learning for sequential decision-makingFairness and equity in resource allocation

Publication output rose steadily from 2017 to a peak in 2021, then settled into a slower but steady pace of about 3-5 papers per year through 2026.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026

Publication cadence
Publications per year over the last 10 years — averaging 4.2/year recently
2017: 2 publications172018: 4 publications182019: 5 publications192020: 8 publications202021: 13 publications13212022: 5 publications222023: 4 publications232024: 5 publications242025: 4 publications252026: 3 publications26
Recent publications
Publishes in
  • arXiv (Cornell University)×10
  • SSRN Electronic Journal×10
  • Manufacturing & Service Operations Management×4
  • Proceedings of the AAAI Conference on Artificial Intelligence×4
  • Operations Research×3
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This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 20, 2026.

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