Yordanka Karayaneva

Papers

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About

Yordanka Karayaneva is a researcher at the forefront of educational robotics and human-robot interaction, with a specialized focus on leveraging autonomous systems to enhance children’s learning. Her work centers on integrating machine learning with social robotics, particularly through the development of object recognition algorithms deployed on the NAO humanoid robot. Karayaneva’s most cited paper, "Object Recognition in Python and MNIST Dataset Modification and Recognition with Five Machine Learning Classifiers" (2018, 46 citations), demonstrates her technical expertise in adapting classic datasets and classifiers for real-world robotic applications. Her complementary study, "Object recognition algorithms implemented on NAO robot for children's visual learning enhancement" (2018), directly applies these methods to create interactive educational tools that help young students identify colors and shapes through social peer-like robotic interaction. By bridging computer vision, machine learning, and pedagogy, Karayaneva’s work has contributed to a growing body of evidence that robots can serve as effective, engaging learning companions. Her research not only advances algorithmic performance but also addresses practical classroom needs, positioning her as a key contributor to the emerging field of child-robot interaction for educational enrichment.