Imran Khan
Papers
4
Total Citations
19
H-Index
2
About
Imran Khan is a rising researcher at the intersection of human-robot interaction, cognitive training, and affective computing. His work centers on how socially assistive robots (SARs) can enhance human cognitive performance and engagement through novel feedback mechanisms. Khan’s major contributions include pioneering the use of differential outcomes training—a technique that pairs distinct rewards with specific responses—in gamified, robot-mediated visuospatial memory tasks, showing that SARs can meaningfully facilitate cognitive training. He has also advanced the understanding of user engagement detection in human-robot interaction, employing deep learning approaches to classify and respond to user states. His comparative analysis of feedback types in companion robots has clarified how task-oriented versus social feedback influences user engagement. With over 19 citations across his most-cited works, Khan’s research is gaining traction for its practical implications in assistive technology and educational robotics. Notably, his 2024 work on human-robot mutual learning through affective-linguistic interaction and differential outcomes training bridges the gap between language-based AI and non-linguistic communication, offering a more holistic framework for designing empathetic, adaptive robots. His findings are informing the next generation of socially intelligent machines.
Research Focus
Key Achievements
Top Papers
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