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 complexity metric for nonconvex rank-one generalized matrix completion
Zhang, Haixiang & Yalcin, Baturalp & Lavaei, Javad & Sojoudi, Somayeh. (2023). A new complexity metric for nonconvex rank-one generalized matrix completion. Mathematical Programming. 10.1007/s10107-023-02008-5.
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.