Arash Mohammadi
Papers
11
Total Citations
218
H-Index
7
About
Arash Mohammadi is a leading researcher at the intersection of multi-agent systems and neural engineering, whose work spans two transformative domains: resilient control theory and neuroprosthetic rehabilitation. In multi-agent systems, he pioneered formation-containment control using dynamic event-triggering mechanisms, significantly reducing communication overhead while maintaining system stability—his foundational paper on this topic has garnered 78 citations. He further advanced the field by developing unified optimization frameworks that ensure consensus under denial-of-service attacks, addressing critical cybersecurity challenges in autonomous networks. Simultaneously, Mohammadi has made groundbreaking contributions to myoelectric control, introducing dilated convolutional neural networks and hybrid deep architectures for surface EMG-based hand gesture recognition, with his 2019 paper receiving 35 citations. His work on real-time hand motion filtering via deep bidirectional RNNs and adaptive tremor estimation using E-BMFLC filters has direct applications in assistive robotics and Parkinson’s disease management. With over 200 total citations across his most-cited works, Mohammadi’s research bridges theoretical control theory with practical neural engineering, offering robust solutions for both autonomous systems and neurorobotic prostheses. His participation in the plenary panel at IEEE ICAS’21 underscores his recognized expertise in shaping the future of autonomous systems.
Research Focus
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Top Papers
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