In this thrust, the Institute focuses on developing novel mathematical models and efficient solution methods to support large-scale multi-stage decision-making under uncertainty. It explores three research avenues:
- Addressing the fundamental challenges of trading off fidelity, tractability, and sample complexity in multi-stage optimization.
- Systematic studies on the complexity analysis and the development of efficient solution approaches for different multi-stage optimization models.
- Exploiting structural properties in probabilitic Uncertainty Quantification (UQ) for multi-stage decision making under uncerta
Publications
Related work
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Spatio-Temporal Point Processes with Attention for Traffic Congestion Event Modeling. Shixiang Zhu, Ruyi Ding, Minghe Zhang, Pascal Van Hentenryck, and Yao Xie. IEEE Transactions on Intelligent Transportation Systems, 2021.
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Communication-Constrained Expansion Planning for Resilient Distribution Systems. Geunyeong Byeon, Pascal Van Hentenryck, Russell Bent, Harsha Nagarajan. INFORMS Journal on Computing, 32(4): 968-985, 2020.
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Dynamic stochastic approximation for multi-stage stochastic optimization. G. Lan and Z. Zhou. Mathematical Programming, 2020. accepted for publication.
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Convex recovery of marked spatio-temporal point processes. Anatoli Juditsky, Arkadi Nemirovski, Liyan Xie, and Yao Xie. arXiv preprint arXiv:2003.12935, 2020.
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Backwash sequence optimization of a pilot-scale ultrafiltration membrane system using data-driven modeling for parameter forecasting. B. Zhang, G. Kotsalis, J. Khan, Z. Xiong, T. Igou, G. Lan, and Y. Chen. Journal of Membrane Science, 612(15), 2020.
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Algorithms for stochastic optimization with function or expectation constraints. G. Lan and Z. Zhou. Computational Optimization and Applications, 76:461–498, 2020.
Decision Making under Uncertainty Team
