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Peyman Mohajerin Esfahani |
Research Interests
My research interests revolve around theoretical and practical aspects of decision-making problems in uncertain and dynamic environments, with applications in engineering systems and management. In my research, I use tools from Control Theory, Optimization, Machine Learning, and Information Theory.
Recent Highlights
Best Lecturer, Systems and Control master program, TU Delft, 2020-2021 & 2021-2022
February 2022: European Control Award, European Control Association (EUCA), 2022
November 2020: Received the INFORMS Frederick W. Lanchester Prize for the best contribution to operations research and management science in the past five years.
September 2020: Awarded the ERC Starting Grant entitled with “Control without Trust: A Distributionally Robust Approach”.
News
April 2025: New paper on Bayesian estimator and active learning for unknonw nonlinear output map
>> Optimal Bayesian Affine Estimator and Active Learning for the Wiener Model, [Code]December 2024: New paper on fault isolation in high-end industrial printers with experimental results
>> Fault Isolation for the Ink Deposition Process in High-End Industrial PrintersDecember 2024: New paper to appear in NeurIPS 2024
>> Scalable Kernel Inverse Optimization, [Code]December 2024: Invited lecture at PhD Winter School on Advanced Stochastic Optimization at NTNU, Norway
November 2024: Updated paper to appear in Transactions on Automatic Control
>> Distributionally Robust Model Predictive Control: Closed-loop Guarantees and Scalable Algorithms, [Code]November 2024: Invited seminar at Workshop on Insurance and Financial Mathematics, Leibnizhaus Hannover, Germany
September 2024: New paper on real-time experiments of robust fault estimation in self-driving cars
>> Robust Fault Estimation with Structured Uncertainty: Algorithms and Experimental Validation
>> Experiment VideoSeptember 2024: Updated paper to appear in Mathematical Programming
>> Nonlinear Distributionally Robust Optimization, [Code]August 2024: Updated paper to appear in Operations Research
>> Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms, [Code]