Paul Grigas is an assistant professor of Industrial Engineering and Operations Research at the University of California, Berkeley. Paul’s research interests are broadly in optimization, machine learning, and data-driven decision making, with particular emphasis on contextual stochastic optimization and algorithms at the interface of machine learning and continuous optimization. Paul’s research is funded by the National Science Foundation, including an NSF CRII Award. Paul was awarded 1st place in the 2020 INFORMS Junior Faculty Interest Group (JFIG) Paper Competition and the 2015 INFORMS Optimization Society Student Paper Prize. He received his B.S. in Operations Research and Information Engineering (ORIE) from Cornell University in 2011, and his Ph.D. in Operations Research from MIT in 2016.
1. Generalization Bounds in the Predict-then-Optimize Framework, with Othman El Balghiti, Adam N. Elmachtoub, and Ambuj Tewari, Mathematics of Operations Research, forthcoming.
2. Smart “Predict, then Optimize,” with Adam N. Elmachtoub, Management Science 68 (1), pp. 9-26, 2022.
3. Risk Bounds and Calibration for a Smart Predict-then-Optimize Method, with Heyuan Liu, Advances in Neural Information Processing Systems (NeurIPS) 34, 2021.
4. Joint Online Learning and Decision-making via Dual Mirror Descent, with Alfonso Lobos and Zheng Wen, Proceedings of the 38th International Conference on Machine Learning (ICML) PMLR 139:7080-7089, 2021.
5. A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives, with Robert M. Freund and Rahul Mazumder, The Annals of Statistics 45 (6), pp. 2328-2364, 2017.