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
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.
A comparative study of the leading machine learning techniques and two new optimization algorithms
Philip Baumann, Dorit S. Hochbaum and Yan T. Yang. A comparative study of the leading machine learning techniques and two new optimization algorithms. European Journal of Operational Research, Volume 272, Issue 3, 1 February 2019, Pages 1041-1057. Online version
Learning Ambidextrous Robot Grasping Policies
Learning Ambidextrous Robot Grasping Policies. Jeffrey Mahler, Matthew Matl, Vishal Satish, Mike Danielczuk, Bill DeRose, Stephen McKinley, Ken Goldberg. Science Robotics Journal. V4(26). Jan 2019. [.pdf].
Robots and the Return to Collaborative Intelligence (Commentary)
Robots and the Return to Collaborative Intelligence (Commentary). Ken Goldberg. Nature Machine Intelligence Journal. volume 1, pages 2–4. January 2019. [.pdf]