Senem Velipasalar

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About

Senem Velipasalar is a leading researcher in computer vision and embedded intelligent systems, with a focus on autonomous navigation and human-robot interaction. Her work on deep learning-based obstacle detection and classification using portable uncalibrated patterned light—cited 36 times—has advanced autonomous systems for visually impaired assistance, assisted driving, and robotics. This contribution addresses critical challenges in real-time obstacle avoidance by combining deep learning with unconventional sensor setups. Velipasalar has also explored the intersection of augmented reality and neurophysiology, as seen in her 2018 study on workload-driven modulation of mixed-reality robot-human communication. This work, with 10 citations, investigates how AR annotations and physiological signals can enhance human-robot collaboration by adapting communication based on cognitive load. Her research bridges practical deployment constraints with intelligent decision-making, making her a key figure in developing safer, more intuitive autonomous systems. Velipasalar’s contributions are particularly notable for their impact on assistive technologies and human-robot interaction, where she continues to push boundaries in real-world applications.