简介：
梁向鹏，算力微电子学院教研助理教授、博士生导师，于2022年获得英国格拉斯哥大学电子与电气工程博士学位，2023年至2025年任清华大学集成电路学院“水木学者”博士后。
梁向鹏博士的研究聚焦于物理储备池计算、神经形态芯片、动态神经网络设计与建模，致力于挖掘电子器件的动态特性用于高效的神经网络计算和信号处理，深入探索器件/电路的机理与算法的结合，推动新一代神经形态计算芯片在机器人控制和低功耗边缘信号处理等场景的应用。
梁向鹏博士近年在Nature Electronics、Nature Communications等国际顶级期刊以第一作者（含共同第一作者）身份发表高水平论文8篇。其中多篇文章入选ESI 热点文章、ESI高被引文章，以及Nature Communications 期刊的 Editors’ Highlights和Neuromorphic Hardware and Computing 专栏等，论文被引1400余次。代表作（Nat. Commun. 2022）提出了物理储备池计算的四种基本架构之一——旋转神经元储备池，完成首个网络层面的“算法演化-硬件行为”等效性证明，并基于该理论开发演示验证系统。此外，他承担国家自然科学基金青年科学基金项目一项，中国博士后科学基金面上项目一项，参与国家重点研发计划项目一项。
研究方向：
（1）物理储备池计算（Physical Reservoir Computing）相关的算法模型、硬件设计及芯片集成；
（2）基于新型半导体器件（如忆阻器）的神经形态感存算一体化系统；
（3）面向连续时序任务与智能仿生闭环控制的全模拟动态神经网络架构。
现面向具有强烈科研热情的优秀人才（博士生、硕士生、博士后、科研助理及实习生）开放招募！
简历投递：liangxiangpeng@suat-sz.edu.cn
Xiangpeng Liangis an Assistant Professor at the Faculty of Computility Microelectronics in Shenzhen University of Advanced Technology (SUAT), China.He received his Ph.D. in Electronics and Electrical Engineering from the University of Glasgow in 2022,followed by a "Shuimu Scholar" Postdoctoral Fellowship at the School of Integrated Circuits, Tsinghua University, from 2023 to 2025.Dr. Liang's research focuses on physical reservoir computing, neuromorphic chips, and the design and modelling of dynamic neural networks. He is dedicated to harnessing the dynamic properties of electronic devices for highly efficient neural network computing and signal processing. By deeply exploring the integration of device/circuit mechanisms with algorithms, he aims to promote the application of next-generation neuromorphic computing chips in scenarios such as robot control and low-power edge computing.
In recent years, Dr. Liang has published 8 high-quality papers as the first author (including co-first author) in top-tier journals such as Nature Electronics and Nature Communications,severalof which have been highlighted as ESI Hot Papers, ESI Highly Cited Papers, as well as featured in the Editors' Highlights and Neuromorphic Hardware and Computing collections of Nature Communications. His publications have been cited over 1,400 times. His featured work (Nat. Commun. 2022) proposed one of the four fundamental architectures of physical reservoir computing—the rotating neuron reservoir (RNR). This work achieved the first network-level equivalence proof between algorithm and hardware behavior and developed a demonstration system based on this theory. In addition, he leads one project funded by the National Natural Science Foundation of China (Youth Science Fund) and one project funded by the China Postdoctoral Science Foundation, and participates in one National Key R&D Program project.
Research Interests:
Physical Reservoir Computing: modelling, hardware design, and chip integration.
Neuromorphic computing integrated systems based on emerging semiconductor devices (e.g., memristors);
Fully analog dynamic neural network architectures for continuous temporal tasks and intelligent biomimetic closed-loop control.
Group openings: We are currently recruiting outstanding talents (Ph.D. students, Master’s students, postdocs, research assistants, and interns) with a strong passion for scientific research!
代表作：
（GoogleScholar: https://scholar.google.com/citations?user=SIhfbhMAAAAJ&hl=en）
X. Liang, J. Tang*, Y. Zhong, B. Gao, H. Qian, H. Wu, “Physical reservoir computing with emerging electronics”,Nature Electronics, 7, 193 (2024).
X. Liang#, Y. Zhong#, J. Tang*, Z. Liu, P. Yao, K. Sun, Q. Zhang, B. Gao, H. Heidari*, H. Qian, H. Wu*, “Rotating neurons for all-analog implementation of cyclic reservoir computing”,Nature Communications, 13, 1549 (2022).
X. Liang, H. Li, A. Vuckovic, J. Mercer, H. Heidari*, “A Neuromorphic Model With Delay-Based Reservoir for Continuous Ventricular Heartbeat Detection”,IEEE Transactions on Biomedical Engineering, 69, 1837 (2022).
H. Huang,X. Liang, Y. Wang, J. Tang, Y. Li, Y. Du, W. Sun, J. Zhang, P. Yao, X. Mou, F. Xu, J. Zhang, Y. Lu, Z. Liu, J. Wang, Z. Jiang, R. Hu, Z. Wang, Q. Zhang, B. Gao, X. Bai, L. Fang, Q. Dai, H. Yin, H. Qian, H. Wu*, “Fully integrated multi-mode optoelectronic memristor array for diversified in-sensor computing”,Nature Nanotechnology, 20, 93 (2025).
E. Covi#*, E. Donati#*,X. Liang#, D. Kappel#*, H. Heidari#*, M. Payvand#, W. Wang#*, “Adaptive Extreme Edge Computing for Wearable Devices”,Frontiers in Neuroscience, 15, 1 (2021).
Y. Zhong, J. Tang*, X. Li,X. Liang, Z. Liu, Y. Li, Y. Xi, P. Yao, Z. Hao, B. Gao, H. Qian, H. Wu*, “A memristor-based analogue reservoir computing system for real-time and power-efficient signal processing”,Nature Electronics, 5, 672 (2022).
