Dr. Pengyi Shi

Associate Professor

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Biography

Professor Pengyi Shi joined Purdue University's Daniels School of Business in January 2014 as an Associate Professor in Operations Management. His research primarily focuses on building data-driven, high-fidelity models and developing predictive and prescriptive analytics to support decision-making under uncertainty in healthcare service systems. Significant streams of his research include developing patient flow models and improving hospital operations and patient outcomes. Shi's expertise has been instrumental during the COVID-19 pandemic, where he applied his research to support inpatient discharge management in Indiana's hospital systems. He has utilized a range of methodologies, including stochastic models, queueing theory, Markov decision processes, and various machine learning techniques to address complexities in healthcare. He has also initiated projects aimed at innovating operations in the criminal justice system through predictive tools. His work has garnered attention and funding for addressing pressing issues such as the nursing shortage and youth substance abuse crisis in diverse communities.

Research Interests

Experience

Associate Professor

2014-01-01 — Present

Purdue University Krannert School of Management • West Lafayette, IN

Teaching and researching operations management with a focus on healthcare and data-driven decision making.

Requirements for Purdue University

Master Program
Requirements
GPA Requirement
Required:3.5
TOEFL
Listening
Required:22
Reading
Required:22
Writing
Required:22
Speaking
Required:22
Total
Required:100
IELTS
Listening
Required:7
Reading
Required:7
Writing
Required:7
Speaking
Required:7
Overall
Required:7.5
Duolingo
Overall Score
Required:135
Overall
Required:135
Prerequisites
Bachelor's degree in CS or related field Foundational concepts of computer science Data structures Computer architecture Operating systems Mathematics through differential equations Linear and matrix algebra
Application Checklist
  • Official Transcripts
  • Academic Statement of Purpose
  • Personal History Statement
  • Resume
  • Three Letters of Recommendation
  • CS Supplementary Form
Specialization Notes

GRE is not required.