Xiangxiang Zeng
Papers
2
Total Citations
34
H-Index
2
About
Xiangxiang Zeng is a pioneering researcher at the intersection of neuromorphic computing, robotics, and bio-inspired intelligence. Her work fundamentally addresses the energy efficiency bottleneck of traditional von Neumann architectures by developing memristive circuit implementations that mimic biological neural networks. Notably, her 2023 paper on implementing Caenorhabditis elegans mechanisms for neuromorphic computing (20 citations) demonstrates how biological neural processing can inspire more efficient computing paradigms. In robotics, Zeng tackles the critical challenge of motion generation for multi-legged robots in complex terrains. Her 2017 work using estimation of distribution algorithms (14 citations) provides innovative solutions for robots to navigate unstructured environments where traditional methods fall short. By combining insights from neuroscience, circuit design, and evolutionary computation, Zeng creates hardware-software co-designs that push the boundaries of both computing efficiency and robotic autonomy. Her interdisciplinary approach—bridging memristive circuit theory with biological neural mechanisms—positions her as a key contributor to the next generation of energy-efficient, adaptive intelligent systems.
Research Focus
Key Achievements
Top Papers
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- 2