Research
IEOR researchers investigate the latest mathematical tools, approaches, and methodologies to make new theoretical discoveries and innovations that touch nearly every industry, making them more efficient and profitable in areas such as supply chain, logistics, manufacturing, data science, energy systems, robotics, and management.
Selected Publications
A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives
Freund, Robert & Grigas, Paul & Mazumder, Rahul. (2015). A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives. The Annals of Statistics. 45. 10.1214/16-AOS1505.
Joint Online Learning and Decision-making via Dual Mirror Descent
Lobos, Alfonso & Grigas, Paul & Wen, Zheng. (2021). Joint Online Learning and Decision-making via Dual Mirror Descent.
Risk Bounds and Calibration for a Smart Predict-then-Optimize Method
Liu, Heyuan & Grigas, Paul. (2021). Risk Bounds and Calibration for a Smart Predict-then-Optimize Method.
Smart “Predict, then Optimize”
Elmachtoub, Adam & Grigas, Paul. (2017). Smart “Predict, then Optimize”. Management Science. 68. 10.1287/mnsc.2020.3922.
Generalization Bounds in the Predict-then-Optimize Framework
El Balghiti, Othman & Elmachtoub, Adam & Grigas, Paul & Tewari, Ambuj. (2019). Generalization Bounds in the Predict-then-Optimize Framework.
Equilibria and incentives for illiquid auction markets
Derchu, Joffrey & Kavvathas, Dimitrios & Mastrolia, Thibaut & Rosenbaum, Mathieu. (2023). Equilibria and incentives for illiquid auction markets.