Safura Sharifi
Papers
3
Total Citations
11
H-Index
3
About
Safura Sharifi is pioneering the intersection of neuromorphic computing and autonomous systems, with a focus on enhancing precision, energy efficiency, and reliability in multi-agent and robotic platforms. Her research centers on developing robust estimation and control frameworks using spiking neural networks (SNNs)—a brain-inspired approach that promises lower power consumption and higher computational efficiency for space robotics and advanced air mobility. In her most-cited work, "Advancing precision in multi-agent systems" (2024, 5 citations), Sharifi introduces an SNN-modified sliding innovation filter to tackle uncertainties in spacecraft rendezvous maneuvers, a critical challenge for satellite servicing and crew transport missions. Her subsequent studies, each garnering 3 citations, extend this neuromorphic paradigm to concurrent estimation and control, addressing the trade-off between computational constraints and task complexity in dynamical systems. By advocating for low size, weight, and power (SWaP) computers, Sharifi is driving the next generation of cost-effective, safe autonomous systems. Her work is particularly notable for bridging theoretical neuroscience with practical engineering, offering a scalable path toward resilient, energy-aware robotics.
Research Focus
Key Achievements
Top Papers
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