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About

Kianoush Nazarpour is a leading researcher at the intersection of neural engineering, myoelectric control, and human-machine interfaces, with a particular focus on advancing prosthetic limb technology and muscle-based control systems. His most influential work has centered on developing sophisticated control strategies for multi-fingered hand prostheses, demonstrating that human subjects can rapidly learn high-dimensional myoelectric control through abstract and proportional paradigms — a breakthrough that earned 98 citations and helped bridge the gap between laboratory research and commercial prosthetics. Nazarpour has made significant contributions to sensory feedback in prosthetics, notably exploring artificial proprioception to reduce users' dependence on visual cues during myoelectric control. His research has progressively evolved toward cutting-edge methodologies, incorporating graph neural networks and explainable AI for high-density EMG-based gesture recognition, reflecting his commitment to transparency and robustness in human-machine systems. His work extends beyond prosthetics into surgical robotics, where he has modeled physiological tremor for real-time compensation in precision microsurgery. More recently, his development of wearable super-resolution muscle-machine interfaces signals an exciting frontier in seamless human-computer interaction. Collectively, his publications have accumulated hundreds of citations, establishing him as a pivotal figure in rehabilitation engineering and neural interfacing.