Qiaosha Zou

Papers

Total Citations

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About

Qiaosha Zou is a leading researcher at the frontier of neuromorphic computing and energy-efficient artificial intelligence. Her work centers on developing spiking neural networks (SNNs) and hierarchical architectures that bridge the gap between biological plausibility and practical hardware implementation. A standout contribution is her pioneering work on "The spike gating flow," a hierarchical structure-based SNN for online gesture recognition, which addresses the critical challenge of high computational cost in deep learning for real-world applications like robotic vision and autonomous vehicles. This paper has garnered 6 citations and exemplifies her focus on creating low-power, real-time AI systems. Dr. Zou's research has significant implications for edge computing and embedded systems, where energy efficiency is paramount. Her achievements include advancing the understanding of spike-based information processing and demonstrating its viability for dynamic, real-world tasks. By tackling the computational bottlenecks of traditional deep learning, Zou is shaping the future of intelligent, sustainable AI that can operate seamlessly in resource-constrained environments, making her a key figure in the evolution of next-generation neural networks.