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
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.
Method of Moments Estimation for Lévy-driven Ornstein-Uhlenbeck Stochastic Volatility Models
Zeyu Zheng, Xiangyu Yang, Yanfeng Wu, and Jian-Qiang Hu. Method of Moments Estimation for Lévy-driven Ornstein-Uhlenbeck Stochastic Volatility Models. Probability in the Engineering and Informational Sciences. https://doi.org/10.1017/S0269964820000315.
ABC-LMPC: Safe Sample-Based Learning MPC for Stochastic Nonlinear Dynamical Systems with Adjustable Boundary Conditions
ABC-LMPC: Safe Sample-Based Learning MPC for Stochastic Nonlinear Dynamical Systems with Adjustable Boundary Conditions. Brijen Thananjeyan*, Ashwin Balakrishna*, Ugo Rosolia, Joseph E. Gonzalez, Aaron Ames, Ken Goldberg. Workshop on the Algorithmic Foundations of Robotics (WAFR), Oulu, Finland, July 2021. [paper]
Statistical consistency of set-membership estimator for linear systems
A. Aswani and P. Hespanhol (2020), Statistical consistency of set-membership estimator for linear systems, IEEE Control Systems Letters, vol. 4, no. 3: 668-673.