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 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 analytical methods to support decisions making under uncertainty in various healthcare systems.

Currently I am studying patient flow models for hospital operations, with a focus on improving inpatient flow management to reduce emergency department (ED) overcrowding. In the past, I have worked on spatial-temporal epidemic models to study the impact of changing viral characteristics and social mixing patterns on the course of influenza pandemic.

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 worked extensively with large-scale data sets from hospitals in the US and Singapore.

Papers Published and Submitted

  • J. G. Dai, P. Shi
    Inpatient Bed Overflow: An Approximate Dynamic Programming Approach.
    Submitted. [Abstract and full paper]

  • J. Feng, P. Shi
    Steady-state Diffusion Approximations for Discrete-time Queue in Hospital Inpatient Flow Management.
    Submitted. [Abstract and full paper]

  • J. G. Dai, P. Shi
    A Two-Time-Scale Approach to Time-Varying Queues for Hospital Inpatient Flow Management.
    forthcoming at Operations Research. 2016. [Abstract and full paper]

  • 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."

  • 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.

  • 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.

  • 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.

  • 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]

Presentations and Posters

  • Overflow Policies for ED Patients Awaiting Inpatient Beds.
    INFORMS Annual Meeting, San Francisco, CA, November, 2014 (scheduled).
    INFORMS Annual Meeting, Minneapolis, MN, October 6, 2013.

  • A Two-time-scale Approach to Time-varying Queues for Hospital Inpatient Flow Management.
    POMS Annual Conference, Atlanta, GA, May 2014.
    INFORMS Healthcare Conference, Chicago, IL, June 26, 2013.
    POMS Annual Conference, Dever, CO, May 3, 2013.
    INFORMS Annual Meeting, Phoenix, AZ, October 15, 2012.
    Extended versions of this work have been given by Prof. Jim Dai at the following places:
    • The opening workshop of Data-Driven Decisions in Healthcare, SAMSI, Raleigh, NC, August 28, 2012.
    • Markov Lecture, INFORMS Annual Meeting, Phoenix, AZ, October 15, 2012.

  • Inpatient Flow Management in a Singaporean Hospital.
    Applied Probability Seminar seminar, School of Industrial and Systems Engineering, Georgia Institute of Technology, February 10, 2012.
    Similar versions of this work have been given by Prof. Jim Dai at the following places:
    • INFORMS Annual Meeting, Charlotte, NC, November 16, 2011.
    • IEOR-DRO Seminars, Columbia University, New York, NY, January 24, 2012.
    • Symposium of Healthcare Decision Making in the Age of Personalised Medicines, Singapore, May 19, 2012.
    • Mostly OM workshop, Beijing, China, June, 2012.

  • The Impact of Mass Gatherings and Holiday Travelling on the Course of an Influenza Pandemic.
    • Annual Conference of Society of Medical Decision Making, Toronto, Canada, October 25, 2010.
    • INFORMS Annual Meeting, Austin, TX, November 7, 2010.
    • Guest lecture for HS 6000 Introduction to Healthcare Delivery, School of Industrial and Systems Engineering, Georgia Institute of Technology, April, 2011.

  • Modeling Seasonality and Viral Mutation to Predict the Course of an Influenza Pandemic.
    • INFORMS Annual Meeting, San Diego, CA, October 2009.
    • Guest lecture for ISYE 4803 Societal/Humanitarian Applications of OR/MS, School of Industrial and Systems Engineering, Georgia Institute of Technology, September 2009.

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