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 system, energy systems, robotics, and management.
Heyuan Liu, Paul Grigas. “New Methods for Regularization Path Optimization via Differential Equations“. NeurIPS 2019 Workshop on Beyond First Order Methods in Machine Learning.
Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor. Daniel Seita, Aditya Ganapathi, Ryan Hoque, Minho Hwang, Edward Cen, Ajay Kumar Tanwani, Ashwin Balakrishna, Brijen Thananjeyan, Jeffrey Ichnowski, Nawid Jamali, Kastu Yamane, Soshi Iba, John Canny, Ken Goldberg.IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, October 2020. [paper], [website].
An Optimally-Competitive Algorithm for Maximum Online Perfect Bipartite Matching with i.i.d. Arrivals
Minjun Chang, Dorit S. Hochbaum, Quico Spaen and Mark Velednitsky. An Optimally-Competitive Algorithm for Maximum Online Perfect Bipartite Matching with iid Arrivals. Theory of Computing Systems, 64 (2020) pp.645-661.
DING AND P. KAMINSKY. 2019.Centralized and Decentralized Warehouse Logistics Collaboration.Manufacturing & Service Operations Management. Extended version available here