Sepeedeh Shahbeigi
Papers
1
Total Citations
2
H-Index
1
About
Sepeedeh Shahbeigi is a researcher at the forefront of safety assurance for vision-based artificial intelligence systems, particularly those deployed in autonomous robots and vehicles. Her work addresses a critical challenge: ensuring that AI-driven perception systems remain robust and reliable even as components degrade or encounter unpredictable, dynamic environments. Shahbeigi’s key contribution lies in pioneering a situation coverage approach that systematically tests and validates the robustness requirements of these systems, moving beyond traditional, static testing methods. Her most-cited paper, "Robustness Requirement Coverage using a Situation Coverage Approach for Vision-based AI Systems" (2025), has already garnered early recognition with 2 citations, signaling its foundational importance in the field. By focusing on how cameras and AI models process environmental data under real-world stressors, Shahbeigi is helping to bridge the gap between theoretical AI safety and practical, deployable autonomy. Her work is essential reading for students and researchers interested in dependable AI, offering a rigorous framework for ensuring that intelligent machines can navigate the complexities of the physical world without failure.
Research Focus
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Top Papers
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