Papers
4
Total Citations
19
H-Index
3
About
Ning Lin’s research bridges the gap between classical robotics control and cutting-edge edge-AI hardware, with a focus on making intelligent systems both precise and efficient. Her early work established a foundation in adaptive control for robot manipulators, where she pioneered the use of radial-basis-function neural networks (RBFNN) to compensate for complex, nonlinear uncertainties in real time. This contribution, detailed in her most cited paper (9 citations), provided a practical, high-performance solution for robotic systems. Later, Lin shifted her attention to the challenges of deploying deep learning on resource-constrained edge devices, such as self-driving cars and smart sensors. Her 2019 paper (5 citations) introduced an auto-masking technique to slim down deep neural networks for efficient on-device inference, addressing critical issues of latency and privacy. Most recently, her 2024 work (1 citation) explores bio-inspired computing, synergizing liquid state machines with RRAM-based analog-digital accelerators to enable few-shot learning for event-based sensors. This trajectory—from robust robot control to efficient edge intelligence—showcases Lin’s ability to tackle foundational control theory while innovating for the next generation of autonomous, low-power systems.
Research Focus
Key Achievements
Top Papers
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