Zahra Rahiminasab

Papers

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About

Zahra Rahiminasab is a researcher at the intersection of machine learning and safety-critical cyber-physical systems (CPS), with a focus on ensuring reliable AI deployment in real-time, autonomous environments. Her most cited work, "Embedded out-of-distribution detection on an autonomous robot platform" (2021, 13 citations), addresses a fundamental challenge in modern CPS: the unreliability of machine learning outputs when encountering novel data outside their training distribution. Rahiminasab’s contributions center on developing practical, embedded methods for out-of-distribution (OOD) detection that can operate within the resource constraints of autonomous platforms. This work is critical for safety-critical applications—such as autonomous robots—where undetected OOD inputs could lead to catastrophic failures. By bridging the gap between theoretical ML robustness and real-time embedded systems, her research provides a pathway for trustworthy AI in autonomous navigation and control. Her impact lies in advancing the reliability of ML-driven CPS, a growing priority as these systems become more pervasive. Rahiminasab’s work is particularly valuable for researchers and engineers seeking to deploy AI in environments where safety and predictability are paramount.