Mahroo Bahreinian

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About

Mahroo Bahreinian is a researcher advancing the safety and interpretability of autonomous systems, with key contributions in control theory and machine learning for robotics. Her work addresses critical challenges in path planning and control under uncertainty, notably through her 2021 paper "Chance Constraint Robust Control with Control Barrier Functions," which has garnered 17 citations. In this influential study, she proposed a novel approach for designing linear feedback controllers that enable robots to navigate polygonal environments safely despite noisy sensor measurements, providing formal stability and safety guarantees—a vital contribution for real-world deployment. Bahreinian also excels in time-series data analysis for autonomous decision-making. Her 2022 work "Classification of Time-Series Data Using Boosted Decision Trees" (11 citations) and her 2021 paper "Inferring Temporal Logic Properties from Data using Boosted Decision Trees" (4 citations) develop interpretable machine learning frameworks. These methods allow robots and self-driving cars to learn temporal logic rules from limited data, enhancing both prediction accuracy and human trust through transparent, verifiable decision-making. By bridging robust control with explainable AI, Bahreinian is shaping the future of safe, reliable autonomous systems that can operate intelligently alongside humans.