Researcher | Guangdong University of Technology | China
Yao Ni is a distinguished researcher specializing in neuromorphic engineering, flexible electronics, artificial synapses, and advanced transistor technologies, with a strong focus on bridging biological intelligence and next-generation electronic systems. His research centers on developing neuromorphic devices, artificial nervous systems, and multifunctional sensing transistors that emulate cognitive, sensory, and neuromuscular behaviors, along with emerging interests in multi-scale reservoir computing, optoelectronic neural interfaces, and intelligent sensing systems for real-world decision-making. Currently serving as a Lecturer at the Guangdong University of Technology, he plays a vital role in advancing research on bio-inspired electronics and smart materials. His work experience showcases numerous collaborations on high-impact projects involving flexible neuromorphic transistors, artificial autonomic nervous systems, light-assisted learning devices, and complex synaptic architectures with multiscale plasticity. Over the years, Ni has significantly contributed to innovations in artificial synapses, hybrid neuromorphic platforms, and ion–electron coupled mechanisms that support advanced forms of memory, perception, and cognitive processing in artificial systems. His impactful research outputs include pioneering stretchable neuromorphic systems for gesture recognition, fiber-based neuromorphic pathways for reflex simulations, programmable photonic-electronic synapses, and neuromuscular devices capable of adaptive environmental responses. He has also contributed to highly cited works in Nature Communications, Advanced Functional Materials, Advanced Science, ACS Nano, and IEEE Electron Device Letters, reflecting his leadership in the field. His contributions extend to the development of low-power neuromorphic circuits, multifunctional sensory neurons, and metal-oxide synaptic devices with enhanced neuroplasticity, supporting innovation in human–machine integration, intelligent robotics, and next-generation computing. Furthermore, Ni’s extensive peer-review activities across more than 30 journals highlight his role in strengthening the global research ecosystem. His impact vision aims to create intelligent, adaptive, and energy-efficient neuromorphic systems that converge electronics with biological intelligence, ultimately influencing advances in healthcare technologies, autonomous robotics, environmental sensing, and future AI hardware. Through his research, Ni strives to enable transformative breakthroughs that promote societal progress, scientific discovery, and industrial applications in emerging electronic and neuromorphic technologies.
Featured Publications
1. Liu, J., Jiang, C., Yu, Q., Ni, Y., Yu, C., & Xu, W. (2025). Multidimensional free shape-morphing flexible neuromorphic devices with regulation at arbitrary points. Nature Communications,. https://doi.org/10.1038/s41467-024-55670-
2. Liu, J., Han, H., Liu, L., Li, Y., Yang, L., & Ni, Y., et al. (2023). Mammalian-brain-inspired neuromorphic motion-cognition nerve achieves cross-modal perceptual enhancement. Nature Communications, —. https://doi.org/10.1038/s41467-023-36935-w
3. Liu, L., Ni, Y., Liu, J., Wang, Y., Jiang, C., & Xu, W. (2023). An artificial autonomic nervous system that implements heart and pupil as controlled by artificial sympathetic and parasympathetic nerves. Advanced Functional Materials, —. https://doi.org/10.1002/adfm.202210119
4. Ni, Y., Yang, L., Feng, J., Liu, J., Sun, L., & Xu, W. (2023). Flexible optoelectronic neural transistors with broadband spectrum sensing and instant electrical processing for multimodal neuromorphic computing. SmartMat, —. https://doi.org/10.1002/smm2.1154
5. Xu, Z., Ni, Y., Han, H., Wei, H., Liu, L., & Zhang, S., et al. (2022). A hybrid ambipolar synaptic transistor emulating multiplexed neurotransmission for motivation control and experience-dependent learning. Chinese Chemical Letters, —. https://doi.org/10.1016/j.cclet.2022.03.015
