Research Projects

Distributionally Robust Optimization

This project aims to address decision-making under uncertainty where the probability distribution of the uncertain parameter is unknown. Our research develops algorithms that ensure reliability and robustness against distribution shifts in real-world applications.

Publications:

Towards Resilient Tracking in Autonomous Vehicles: A Distributionally Robust Input and State Estimation Approach Kasra Azizi, Wenbin Wan Work under review

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