Tayyabah Hasan

Papers

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About

Tayyabah Hasan is a researcher at the forefront of cognitive robotics and human-robot interaction, with a particular focus on trust dynamics and attention mechanisms in cloud-integrated systems. Her most-cited work, "Trust identification through cognitive correlates with emphasizing attention in cloud robotics" (2022), makes a significant contribution by establishing cognitive correlates between selective attention and trust perception in robotic sensory processing. This research demonstrates how robots can prioritize competing stimuli through attention-based filtering, enhancing their ability to build and maintain trust with human collaborators. With 9 citations to date, this paper has already influenced emerging discussions on cognitive architectures for cloud robotics. Hasan's work bridges the gap between computational attention models and real-world robotic decision-making, offering practical frameworks for designing more intuitive and trustworthy autonomous systems. Her research is particularly valuable for students and engineers working on human-robot collaboration, as it provides concrete methodologies for integrating cognitive principles into robotic perception. By addressing the fundamental challenge of how robots allocate attention in complex environments, Hasan is helping shape the next generation of socially aware and reliable robotic assistants.