Papers
8
Total Citations
157
H-Index
5
About
Enkelejda Kasneci is a prominent researcher at the intersection of human-robot interaction, eye tracking, and multimodal AI systems. Her work focuses on leveraging gaze and cognitive signals to create more intuitive, intelligent collaborative systems between humans and robots — a field with profound implications for assistive technologies and autonomous systems. Kasneci's most-cited contribution, "Eye-Hand Behavior in Human-Robot Shared Manipulation" (2018, 68 citations), demonstrates her pioneering role in shared autonomy, showing how gaze analysis can dramatically improve robots' ability to infer human intent during physical collaboration. This work has shaped how researchers approach intention recognition in assistive robotics. Her transfer learning research on automatic video annotation (49 citations) further reflects her versatility, bridging computer vision and machine intelligence. A recurring theme across her portfolio is using gaze as a communicative bridge — whether calibrating robots through augmented reality, teaching robots to recognize unknown objects via shared gaze, or modeling cognitive states through multimodal signals. Her 2020 and 2022 work on gaze-based object detection addresses one of robotics' most persistent challenges: enabling robots to operate in open-ended, unpredictable environments. With research spanning eye tracking, human-robot collaboration, and cognitive modeling, Kasneci has established herself as a distinctive voice in making human-machine interaction more natural, adaptive, and responsive.
Research Focus
Key Achievements
Top Papers
- 1Eye-Hand Behavior in Human-Robot Shared Manipulation68 citations · 2018
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- 5Modeling Cognitive Processes from Multimodal Signals5 citations · 2018
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- 7Gaze-based Object Detection in the Wild3 citations · 2022
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