Pengyi Shi

Assistant Professor of Operations Management
Krannert School of Management
Purdue University
Email: shi178 AT purdue DOT edu

[CV]

    Pengyi Shi


About me

I am an Assistant Professor of Operations Management at the Krannert School of Management, Purdue University. I joined Krannert in Janurary 2014 and my tenure will be effective in August 2021. I am an affiliate faculty of the Regenstrief Center for Healthcare Engineering and the Integrative Data Science Initiative.

I obtained my Ph.D. degree from the School of Industrial and Systems Engineering at Georgia Institute of Technology in December 2013. I received my B.Eng. in Applied Mathematics and Mechanics and B.A. in Economics (double major) from Peking University, China, in June 2007. My Ph.D. advisors are Jim Dai and Pinar Keskinocak. Here is a copy of my Ph.D. thesis [pdf].


Research

My research focuses on building data-driven, high fidelity models and developing predictive and prescriptive analytics to support decisions making under uncertainty in healthcare and service systems.

One of my main research stream is to develop patient flow models to improve hospital operations and patient outcomes. This stream of research has been implmeneted as tools for supporting inpatient discharge management and for supporting COVID-19 response in the hospital systems in Indiana. Recently, I have started working on developing predictive and operations tool for criminal justice system.

My research methodologies include stochastic models, queueing theory, Markov decision process, machine learning, reinforcement learning, and online learning. See my publications and here for a guest lecture I gave on applying reinforcement learning to tackle large-scale decision problems in hospital operations and criminal justice system. I am currently serving as an Associate Editor of Operations Research, Health Care Management Science, Service Science, and Decision Sciences.

I strive to develop high impact research and tackle real-world problems. I have been collaborating with practitioners and faculty members from different healthcare organizations and goverment agencies, publishing on medical journals. I have worked extensively with large-scale data sets from hospitals in the US, Singapore, and China.

I have had the pleasure of working closely with PhD students and undergraduate students. Graduate students with solid theoretical background could check out this research opportunity Deep-learning Enhanced Healthcare Modeling and Optimization. Purdue undergraduate students are encouraged to apply through my DURI project.


Publication

Working Papers

denotes industry/medical collaborator; underline denotes student
  1. P. Shi, J. E. Helm, C. Chen, J. Lim, R. Parker, T. Troy, and J. Cecil
    Operations (Management) Warp Speed: Parsimonious Design for Rapid Deployment of Hospital Prediction and Decision Support Framework during a Pandemic.
    Under major revision. [Abstract and full paper]
    • I am the sole academic researcher in developing the adaptive workload/census prediction, which is a core component of the integrated tool for COVID-19 surge planning.
    • This tool has been implemented in IU Health (the largest hospital systems in Indiana) since April 2020 to support their COVID-19 response. Below are several selected news release on our work:

  2. Y. Liu, P. Shi, J. E. Helm, M. P. Van Oyen, L. Ying, and T. Hucshka
    Coordinated Care: Capacity Allocation to Improve Itinerary Completion in Queueing Networks.
    Revision Submitted. [Abstract and full paper]

  3. H. Bastani, P. Shi
    Proceed with Care: Integrating Predictive Analytics with Patient Decision-Making.
    Invited book chapter for Modeling for Health: Making Changes. [Draft]

  4. J. Dong, P. Shi, F. Zheng, and X. Jin
    Off-service Placement in Inpatient Ward Network: Resource Pooling versus Service Slowdown.
    Preparing resubmission to Manufacturing and Service Operations Management. [Abstract and full paper]
    • Second place, College of Healthcare Operations Management Best Paper Award, POMS, 2020
    • Selected to 2019 MSOM Conference Healthcare SIG

  5. X. Liu, B. Li, P. Shi, L. Ying
    An Efficient Pessimistic-Optimistic Algorithm for Constrained Linear Bandits.
    [Abstract and full paper]

Papers Accepted and Published in Operations Journals

denotes industry/medical collaborator; underline denotes student
  1. J. Chen, J. Dong, P. Shi
    A Survey on Skill-Based Routing with Applications to Service Operations Management.
    Queueing Systems 2020; 4:1-30.

  2. P. Shi, J. E. Helm, S.H. Heese, and A. Mitchell
    An Operational Framework for the Adoption and Integration of New Diagnostic Tests.
    Production and Operations Management. Forthcoming. [Abstract and full paper]

  3. P. Shi, J. E. Helm, J. Deglise-Hawkinson, and J. Pan
    Timing it Right: Balancing Inpatient Congestion versus Readmission Risk at Discharge.
    Operations Research. Forthcoming. [Abstract and full paper]
    • First place, Pierskalla Best Paper Award, INFORMS HAS, 2018
    • Second place, Innovative Applications in Analytics Award, INFORMS Analytics, 2020
    • Second place, College of Healthcare Operations Management Best Paper Award, POMS, 2019
    • Selected to 2019 CHOM Mini Conference Showcase Presentations
    • Selected to 2019 MSOM Conference Healthcare SIG
    • Pilot implementation of the tool at a local hospital in Indiana. Also see a video on this research and a newsletter article.

  4. J. G. Dai, P. Shi
    Recent Modeling and Analytical Advances in Hospital Inpatient Flow Management.
    Production and Operations Management. Forthcoming. [Abstract and full paper]
    • Invited paper for special issue on Frontier Analytic Modeling and Methods for OM.

  5. A. Alaeddini, J. E. Helm, P. Shi, and S. Faruqui
    An integrated framework for reducing hospital readmissions using risk trajectories characterization.
    IISE Transactions on Healthcare Systems Engineering. 2019; 9(2):172-185.
    • Former title: "A Prediction Model for Patient Readmission Risks with Kernel Principle Component Analysis."

  6. J. G. Dai, P. Shi
    Inpatient Bed Overflow: An Approximate Dynamic Programming Approach.
    Manufacturing and Service Operations Management. 2019; 21(4):894-911. [Abstract and full paper]

  7. J. Feng, P. Shi
    Steady-state Diffusion Approximations for Discrete-time Queue in Hospital Inpatient Flow Management.
    Naval Research Logistics. 2018; 65(1):26-65. [Abstract and full paper]

  8. J. G. Dai, P. Shi
    A Two-Time-Scale Approach to Time-Varying Queues for Hospital Inpatient Flow Management.
    Operations Research. 2017; 65(2):514-36. [Abstract and full paper]

  9. P. Shi, M. Chou, J. G. Dai, D. Ding, and J. Sim
    Models and Insights for Hospital Inpatient Operations: Time-Dependent ED Boarding Time.
    Management Science. 2016; 62(1):1-28. [Main Paper] [Online Supplement]
    • Former title: "Hospital Inpatient Operations: Mathematical Models and Managerial Insights."

Medical Publication

denotes industry/medical collaborator; underline denotes student
  1. F. Dexter, R. H. Epstein, P. Shi
    Proportions of Outpatients Discharged the Same or the Following Day are Sufficient Data to Guide the Assessment of the Cases.
    Cureus. 2021; 13 (3).

  2. F. Dexter, R. H. Epstein, P. Shi
    Forecasting the Probability That Patient Will Remain in the Hospital Overnight Versus Have a Length of Stay of 2 or More Days.
    Cureus. 2020; 12(10): e10847.

  3. P. Shi, J. Yan, P. Keskinocak, A. L Shane, J. L Swann
    The Impact of Opening Dedicated Clinics on Disease Transmission during an Influenza Pandemic.
    PLoS ONE. 2020; 15(8): e0236455.

  4. P. Shi, F. Dexter, R. H. Epstein
    Comparing Policies for Case Scheduling Within One Day of Surgery by Markov Chain Models.
    Anesthesia & Analgesia. 2016; 122(2):526-38.

  5. F. Dexter, P. Shi, R. H. Epstein
    Descriptive study of case scheduling and cancellations within one week of the day of surgery.
    Anesthesia & Analgesia. 2012; 115(5): 1188-95.

  6. P. Shi, P. Keskinocak, J. L. Swann, B. Y. Lee
    The Impact of Mass Gatherings and Holiday Travelling on the Course of an Influenza Pandemic: A Computational Model.
    BMC Public Health. 2010; 10: 778.

  7. P. Shi, P. Keskinocak, J. L. Swann, B. Y. Lee
    Modeling Seasonality and Viral Mutation to Predict the Course of an Influenza Pandemic.
    Epidemiology and Infection. 2010; pp. 1-10.

Other Papers

  • P. Shi, J. G. Dai, D. Ding, J. Ang, M. Chou, X. Jin and J. Sim
    Patient Flow from Emergency Department to Inpatient Wards: Empirical Observations from a Singaporean Hospital.
    This is a 69-page document on a comprehensive empirical study of inpatient flow management. [pdf]

Contact Information

403 W State St Kran 472
Krannert School of Management
Purdue University
West Lafayette, IN 47907
Office phone: (765) 494-0458
Email: shi178 at purdue.edu