Eman Hassan
Papers
2
Total Citations
33
H-Index
2
About
Eman Hassan is a pioneering researcher in neuromorphic vision and robotic grasping, whose work bridges the gap between biological inspiration and practical automation. Her key research areas include event-based vision, tactile sensing, and hyperdimensional computing for robotics. Hassan’s major contributions center on developing low-latency, energy-efficient perception systems that enable robots to classify contact properties and infer object characteristics during grasping tasks. Her most-cited paper, "Neuromorphic Vision Based Contact-Level Classification in Robotic Grasping Applications" (2020, 26 citations), introduced a novel approach that leverages neuromorphic sensors for industrial sorting, dramatically reducing power consumption and response times compared to traditional methods. Building on this, her 2024 work "Efficient event-based robotic grasping perception using hyperdimensional computing" (7 citations) further advanced the field by integrating Dynamic and Active Vision Sensors (DAVIS) with hyperdimensional computing, achieving superior energy efficiency for real-time object property inference. Hassan’s research is notable for its direct industrial applicability, addressing critical challenges in robotic sorting and grasping quality. Her work stands out for combining cutting-edge neuromorphic hardware with innovative computing paradigms, positioning her as a key figure in the next generation of intelligent, low-power robotic systems.
Research Focus
Key Achievements
Top Papers
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