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About

Maryam Rezayati is a leading researcher in human–robot collaboration, with a focus on creating safe, adaptive industrial automation systems. Her work addresses the critical challenge of integrating flexible, human-centered manufacturing—central to the Industry 5.0 paradigm—while ensuring worker safety in shared workspaces. Her most influential paper, “A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation” (2020, 76 citations), pioneered a multi-sensor framework that enables robots to perceive and respond to human presence dynamically, balancing productivity with risk mitigation. She further advanced this field with “Improving Safety in Physical Human-Robot Collaboration via Deep Metric Learning” (2022), which uses machine learning to predict and prevent hazardous contacts, and “Human-Robot Contact Detection in Assembly Tasks” (2022), which enhances robot perception for real-time collision awareness. Collectively, her research has garnered nearly 110 citations, underscoring its impact on both academia and industry. Rezayati’s contributions are vital for the next generation of smart factories, where humans and robots work side by side safely and efficiently.