Maha Shatta

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About

Maha Shatta is a rising researcher at the forefront of neuromorphic computing, specializing in the intersection of biologically-inspired hardware and printed electronics. Her work focuses on developing energy-efficient, low-cost computing solutions for emerging applications such as soft robotics, wearables, and IoT devices. Shatta’s most-cited paper, “Analog Printed Spiking Neuromorphic Circuit” (2024), introduces a novel approach to implementing Spiking Neural Networks (SNNs) using printed electronics—a field that promises highly customizable, cost-effective hardware. This work has already garnered 5 citations, signaling early impact in a nascent area. By combining the energy efficiency of SNNs with the flexibility of printed circuits, Shatta is addressing critical challenges in deploying intelligent systems in resource-constrained environments. Her research bridges materials science, circuit design, and computational neuroscience, offering a pathway toward scalable, sustainable neuromorphic systems. As the demand for adaptive, low-power hardware grows, Shatta’s contributions position her as a key innovator in the future of edge computing and intelligent embedded systems.