Andrew Li

Andrew Li is an Associate Professor of Operations Research at Carnegie Mellon University's Tepper School of Business since 2024, previously serving as Assistant Professor since 2018. His research bridges statistics, optimization, and machine learning with applications to healthcare operations and retail management.

• Current teaching: Optimization, Business Analytics Capstone, and Topics in Optimization and Statistics
• PhD from MIT's Operations Research Center (2018), BS in Operations Research/Applied Mathematics from Columbia University (2012)

Research Focus: Dr. Li develops data-driven decision frameworks for complex systems. Key areas include:

• Experience-based learning models with fairness constraints (organ allocation)
• Anomaly detection in low-rank matrices (retail inventory accuracy)
• Nanoparticle-based diagnostic systems for CAD and Alzheimer's
• Nonstationary demand forecasting in supply chains

Publication Trends: Recent work combines bandit algorithms with healthcare applications (split liver transplants, CAD detection) and retail operations (inventory accuracy). Theoretical contributions include regret-optimal policies and entrywise anomaly detection guarantees.

Scientific Honors:

• INFORMS Nicholson Award (2018)
• INFORMS Pierskalla Award (2021)
• NSF CAREER Award (2023)

Professional Leadership: Active in INFORMS and CMU committees including MBA Analytics Curriculum, Thompson Award, and ENAiBLE AI-driven retail collaborative co-founder since 2021.