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Daniels School Faculty

Pengyi Shi

Pengyi Shi

Associate Professor of Management

CV

Professor Shi joined Purdue's school of business in January 2014. She is an Associate Professor of Operations Management. She is also a faculty affiliate of the Regenstrief Center for Healthcare Engineering and the Integrative Data Science Initiative. She received her Ph.D. from the School of Industrial and Systems Engineering at Georgia Institute of Technology.

Professor Shi's 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 her main research streams is to develop patient flow models to improve hospital operations and patient outcomes. This stream of research has been implemented as tools for supporting inpatient discharge management and for supporting COVID-19 response in the hospital systems in Indiana. Recently, she has started working on developing predictive and operations tools for the criminal justice system.

Her research methodologies include stochastic modelsqueueing theoryMarkov decision processmachine learningreinforcement learning, and online learning. See her full publications at https://web.ics.purdue.edu/~shi178/.

  • Innovating Healthcare: Addressing the Nursing Shortage with Data Analytics

    The longstanding nurse shortage has risen to the level of a global health crisis, with the U.S. projected to face a deficit of half a million nurses by 2025, according to Pengyi Shi, an associate professor at Purdue's Mitchell E. Daniels, Jr. School of Business. Combined with spikes in demand, the accelerating shortage has prompted hospitals and health systems to explore innovative solutions.

    Full story: Innovating Healthcare: Addressing the Nursing Shortage with Data Analytics

  • hospital patient in hallroom

    No Room in the Hospital? An Analytical Tool Helps Doctors Decide Which Patients to Discharge

    The COVID-19 pandemic has put immense stress on hospitals around the country, many of them struggling to provide enough beds to accommodate the surge in patients. To create room for all these new patients, hospitals have been forced to discharge existing patients earlier than expected. Sending them home early alleviates overcrowding and reduces costs, but it may put their health at risk, increasing the chances that they'll be back in hospital beds within a few weeks.

    Full story: No Room in the Hospital? An Analytical Tool Helps Doctors Decide Which Patients to Discharge

  • Reducing Hospital Readmissions

    Professor Pengyi Shi on using data analytics to reduce hospital readmissions and congestion

Contact

shi178@purdue.edu
Phone: (765) 494-0458
Office: KRAN 472

Quick links

Personal website

Area(s) of Expertise

Artificial Intelligence, Business Analytics, Crime, Data Analytics, Healthcare, Machine Learning, Operations Management, Public Policy