Rasha Shoitan

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About

Dr. Rasha Shoitan is a leading researcher in intelligent automation and sustainable engineering, whose work bridges computer vision and robotics to address critical environmental challenges. Her most-cited paper, "Object Detection-based Automatic Waste Segregation using Robotic Arm" (2023, 18 citations), exemplifies her core contribution: developing smart, scalable systems that leverage deep learning for real-world waste management. By integrating object detection algorithms with robotic manipulation, Shoitan has pioneered cost-effective solutions for automated garbage segregation, directly tackling the inefficiencies that plague developing nations facing rapid urbanization. Her research demonstrates how AI-driven robotics can transform municipal waste handling—reducing human exposure to hazardous materials and improving recycling rates. With growing recognition for her practical, impact-oriented approach, Shoitan continues to advance the intersection of computer vision and mechatronics, producing work that is equally relevant to academic researchers and urban planners. Her innovations offer a blueprint for creating sustainable, technology-enabled communities in resource-constrained settings.