Yaser Mike Banad
Papers
3
Total Citations
11
H-Index
3
About
Yaser Mike Banad is pioneering the intersection of neuromorphic computing and autonomous systems, with a focus on enhancing precision, energy efficiency, and reliability in multi-agent and space robotic missions. His most cited work introduces a spiking neural network-modified sliding innovation filter for multi-spacecraft rendezvous, addressing critical uncertainties in space robotic modeling—a contribution that has already garnered 5 citations since 2024. Banad’s research tackles the growing demand for low size, weight, and power (SWaP) computers in industries like space robotics and advanced air mobility, as evidenced by his 2023 study on energy-efficient estimation in autonomous systems (3 citations). He further advances the field with a neuromorphic robust framework for concurrent estimation and control in dynamical systems (3 citations), balancing computational constraints with task complexity. By leveraging biologically inspired spiking neural networks, Banad is redefining how autonomous systems handle noisy, uncertain environments—paving the way for safer, more cost-effective missions. His work is particularly notable for its direct application to crew transport and satellite tasks, where precision and reliability are paramount.
Research Focus
Key Achievements
Top Papers
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