Yaqin Zhang

Papers

Total Citations

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About

Yaqin Zhang is a rising researcher in computational imaging, with a primary focus on non-line-of-sight (NLOS) imaging—a transformative technology with applications in autonomous vehicles, robotic vision, and biomedical imaging. Her most cited work, "Accurate but fragile passive non-line-of-sight recognition" (2021, 23 citations), addresses a critical challenge in the field: enabling robust classification of hidden objects without direct line-of-sight. Zhang’s research explores the delicate balance between accuracy and reliability in passive NLOS recognition systems, highlighting both the potential and limitations of current methods. By investigating how environmental factors and system fragility affect performance, she provides essential insights for developing more resilient imaging solutions. Though early in her career, Zhang’s contributions are already shaping discussions around practical NLOS deployment, particularly in safety-critical contexts like autonomous navigation. Her work underscores the importance of understanding system vulnerabilities alongside performance metrics, offering a nuanced perspective that bridges theoretical advances with real-world constraints. For students and researchers in computational imaging, Zhang’s research serves as a vital reminder that breakthrough technologies must also withstand the unpredictability of real environments.