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
Stochastic In-Face Frank-Wolfe Methods for Non-Convex Optimization and Sparse Neural Network Training
Paul Grigas, Alfonso Lobos, Nathan Vermeersch. “Stochastic In-Face Frank-Wolfe Methods for Non-Convex Optimization and Sparse Neural Network Training”. arXiv:1906.03580
Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery
Richard Zhang, Somayeh Sojoudi, and Javad Lavaei. Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery. Journal of Machine Learning Research, 2019. https://jmlr.org/papers/v20/19-020.html.
Data-driven incentive design in the Medicare Shared Savings Program
Anil Aswani, Zuo-Jun Max Shen, Auyon Siddiqui, (2019), Data-Driven Incentive Design in the Medicare Shared Savings Program, INFORMS Operations Research, Vol. 67, No. 4.
Modeling differentiation-state transitions linked to therapeutic escape in triple-negative breast cancer
Margaret P., Tyler R., Anil A., et.al. “Modeling differentiation-state transitions linked to therapeutic escape in triple-negative breast cancer”. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1006840.
A scalable approach to enhancing stochastic kriging with Gradients
Haojun Huo; Xiaowei Zhang; Zeyu Zheng. “A scalable approach to enhancing stochastic kriging with Gradients”. Proceedings of the Winter Simulation Conference.