Papers
3
Total Citations
80
H-Index
3
About
Dr. Su Yang is a leading researcher at the intersection of neuromorphic computing and intelligent robotics, with a primary focus on developing bio-inspired autonomous learning systems. Their most significant contributions lie in advancing Spiking Neural Networks (SNNs)—the third generation of artificial neural networks—for real-world robotic applications. Dr. Yang's pioneering work, "Spiking neural network-based multi-task autonomous learning for mobile robots" (2021, 36 citations), demonstrates how SNNs can enable robots to learn multiple tasks simultaneously with remarkable efficiency, mimicking biological neural processing. They further extended this paradigm with "Bio-Inspired Autonomous Learning Algorithm With Application to Mobile Robot Obstacle Avoidance" (2022, 9 citations), showcasing SNNs' superior information processing capabilities for dynamic environments. In a notable departure from neuromorphic methods, Dr. Yang also achieved significant impact with "Traffic signal control using reinforcement learning based on the teacher-student framework" (2023, 35 citations), introducing a novel knowledge distillation approach that dramatically improves urban traffic flow. This work highlights their versatility in applying advanced machine learning to complex infrastructure problems. With over 80 total citations and growing, Dr. Yang's research bridges fundamental neuroscience principles with practical engineering, offering transformative solutions for autonomous systems and smart cities.
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