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
Conic Optimization for Quadratic Regression Under Sparse Noise
Molybog, Ramtin Madani, and Javad Lavaei. Conic Optimization for Quadratic Regression Under Sparse Noise. Journal of Machine Learning Research. https://www.jmlr.org/papers/v21/18-881.html.
Simulating Polyculture Farming to Tune Automation Policies for Plant Diversity and Precision Irrigation
Simulating Polyculture Farming to Tune Automation Policies for Plant Diversity and Precision Irrigation. Yahav Avigal, William Wong, Jensen Gao, Kevin Li, Mark Theis, Mark Preston, Grady Pierroz, Fang Shuo Deng, Ken Goldberg. Winner of Best Student Paper Award. 2020 IEEE Conference on Automation Science and Engineering (CASE), Online (Hong Kong) Aug 20-21, 2020. [Paper] [Presentation Video (15 mins)]
Simulating Nonstationary Spatio-Temporal Poisson Processes using the Inversion Method
Zhang, Haoting and Zheng, Zeyu, Simulating Nonstationary Spatio-Temporal Poisson Processes using the Inversion Method (July 27, 2020). Available at SSRN: https://ssrn.com/abstract=3661101 or http://dx.doi.org/10.2139/ssrn.3661101
An Efficient Homotopy Method for Solving the Post-Contingency Optimal Power Flow to Global Optimality
Park, Sangwoo & Glista, Elizabeth & Lavaei, Javad & Sojoudi, Somayeh. (2022). An Efficient Homotopy Method for Solving the Post-Contingency Optimal Power Flow to Global Optimality. IEEE Access. PP. 1-1. 10.1109/ACCESS.2022.3224162.
When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment
Feng Zhu, Zeyu Zheng. When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment. International Conference on Machine Learning (ICML) 2020. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3637905.
On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification
Tianyi Lin, Zeyu Zheng, Elynn Y. Chen, Marco Cuturi, and Michael I. Jordan. On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification. International Conference on Artificial Intelligence and Statistics (AISTATS) 2021. https://arxiv.org/abs/2006.12301.