Faculty
Dr. Zhongrui Wang is a tenured associate professor at the School of Microelectronics at Southern University of Science and Technology, a awardee of the NSFC Excellent Youth Fund (Hong Kong and Macau), and a Clarivate Highly Cited Researcher. Prior to joining SUSTech, he was an assistant professor in the Department of Electrical and Electronic Engineering at the University of Hong Kong. He earned his Bachelor's degree (First Class Honors) and Ph.D. from Nanyang Technological University in Singapore.
Dr. Wang's research primarily focuses on novel AI hardware and algorithm co-design. He has published papers as a corresponding or first author in journals such as Nature Reviews Materials, Nature Materials, Nature Electronics (4 papers), Nature Machine Intelligence (2 papers), Nature Computational Science (2 papers), Nature Communications (3 papers), Science Advances (3 papers), as well as conferences like DAC (6 papers), ICCAD (2 papers), ICCV and IEDM.
His work has received over 19,000 citations on Google Scholar (h-index of 49) and has been featured in over 40 news outlets, including IEEE Spectrum, Scientific American, Science Daily, Phys.org, and ACM Communications.
Dr. Wang was a member of the IEEE Electron Devices Society's Nanotechnology Committee and serves on the editorial boards of journals such as InfoMat, Materials Today Electronics, Frontiers in Neuroscience, and APL Machine Learning.
Email: [email protected]. For more information, please visit https://zhongruiwang.github.io/.
Education
2014, Ph.D., Nanyang Technological University, Singapore
2009, Bachelor's Degree (First Class Honors), Nanyang Technological University, Singapore
Work Experience
2024–Present, Tenured Associate Professor, Southern University of Science and Technology
2020–2024, Assistant Professor, University of Hong Kong
2014–2020, Postdoctoral Researcher, University of Massachusetts Amherst
Awards
Clarivate highly cited researchers (2023 and 2024, 1 out of 16 in SUSTech)
Best poster award, Nature Conferences on Neuromorphic Computing (2025)
Research Interests
(Students with a background in machine learning, computer architecture, or math/physics/statistics are welcome to apply)
· Novel AI hardware and its software co-design
· Applications (Efficient embodied AI, agents)
Papers
（Google Scholar：https://scholar.google.com/citations?user=Ofl3nUsAAAAJ）
（ResearchGate: https://www.researchgate.net/profile/Zhongrui-Wang-2）
Recent representative works
1. S. Wang?, Y. Li?, D. Wang, W. Zhang, X. Chen, D. Dong, S. Wang, X. Zhang, P. Lin, C. Gallicchio, X. Xu, Q. Liu, K.-T. Cheng, Z. Wang*, D. Shang*, M. Liu, Echo state graph Neural Networks with Analogue Random Resistor Arrays, Nature Machine Intelligence, 5, 104 (2023) [Main corresponding author]
2. N. Lin?, S. Wang?, Y. Li?, B. Wang, S. Shi, Y. He, W. Zhang, Y. Yu, Y. Zhang, X. Qi, X. Chen, H. Jiang, X. Zhang, P. Lin, X. Xu, Q. Liu, Z. Wang*, D. Shang*, M. Liu, Resistive memory-based zero-shot liquid state machine for multimodal event data learning, Nature Computational Science, 5, 37 (2025) [Main corresponding author]
3. M. Xu, S. Wang, Y. He, Y. Li, W. Zhang, M. Yang, X. Qi*, Z. Wang*, M. Xu*, D. Shang*, Q. Liu, X. Miao, M. Liu, Efficient modelling of ionic and electronic interactions by resistive memory-based reservoir graph neural network, Nature Computational Science (In Press) [Main corresponding author]
4. S. Wang?, Y. Gao?, Y. Li?, W. Zhang, Y. Yu, B. Wang, N. Lin, H. Chen, Y. Zhang, Y. Jiang, D. Wang, J. Chen, P. Dai, H. Jiang, P. Lin, X. Zhang, X. Qi, X. Xu, H. So, Z. Wang*, D. Shang*, Q. Liu, K-T. Cheng, Ming Liu, Random resistive memory-based deep extreme point learning machine for unified visual processing, Nature Communications, 16, 960 (2025). [Main corresponding author]
5. Y. Zhang?, W. Zhang?, S. Wang, N. Lin, Y. Yu, Y. He, B. Wang, H. Jiang, P. Lin, X. Xu, X. Qi, Z. Wang*, X. Zhang*, D. Shang*, Q. Liu, K.-T. Cheng, M. Liu, Dynamic neural network with memristive CIM and CAM for 2D and 3D vision, Science Advances, 10, eado1058 (2024) [Main corresponding author]
6. B. Wang, X. Zhang, S. Wang, N. Lin, Y. Li, Y. Yu, Y. Zhang, J. Yang, X. Wu, Y. He, S. Wang, T. Wan, R. Chen, G. Li, Y. Deng, X. Qi*, Z. Wang*, D. Shang*, Topology optimization of random memristors for input-aware dynamic SNN. Science advances. 11. eads5340 (2025) [Main corresponding author]
7. H. Chen?, J. Yang?, J. Chen?*, S. Wang, S. Wang, D. Wang, X. Tian, Y. Yu, X. Chen, Y. Lin, Q. Zhu, Y. He, X. Wu, Y. Li, X. Zhang, N. Lin, M. Xu, X. Zhang, X. Qi, Z. Wang*, H. Wang*, D. Shang*, Q. Liu, K.-T. Cheng, M. Liu, Continuous-Time Digital Twin with Analogue Memristive Neural Ordinary Differential Equation Solver, Science Advance 11, eadr7571 (2025) [Main corresponding author]
8. J. Yang?, H. Chen?, J. Chen?*, S. Wang, S. Wang, Y. Yu, X. Chen, B. Wang, X. Zhang, B. Cui, Y. Li, N. Lin, M. Xu, Y. Li, X. Xu, X. Qi, Z. Wang*, X. Zhang*, D. Shang*, H. Wang, Q. Liu, K.-T. Cheng, M. Liu, Resistive memory-based neural differential equation solver for score-based diffusion model, ArXiv: 2404.05648 https://arxiv.org/abs/2404.05648 [Main corresponding author]
9. Y. Yu, S. Wang, W. Zhang, X. Zhang, X. Wu, Y. He, J. Yang, Y. Zhang, N. Lin, B. Wang, X. Chen, S. Wang, X. Zhang, X. Qi, Z. Wang*, D. Shang*, Q. Liu*, K.-T. Cheng, M. Liu, Efficient and accurate neural field reconstruction using resistive memory, ArXiv: 2404.09613 https://arxiv.org/abs/2404.09613 [Main corresponding author]
10. S. Wang?, X. Chen?, C. Zhao, Y. Kong, B. Lin, Y. Wu, Z. Bi, Z. Xuan, T. Li, Y. Li, W. Zhang, E. Ma, Z. Wang*, W. Ma*, Molecular-scale integration of multi-modal sensing and neuromorphic computing with organic electrochemical transistors, Nature Electronics, 6, 281 (2023) [Co-corresponding author]
11. D. Liu?, X. Tian?, J. Bai?, S. Wang?, S. Dai, Y. Wang, Z. Wang*, S. Zhang*, A wearable in-sensor computing platform based on stretchable organic electrochemical transistors. Nature Electronics, 7, 1176 (2024) [Co-corresponding author]
Other representative works
1. Z. Wang, H. Wu, G. W. Burr, C. S. Hwang, K. L. Wang, Q. Xia*, and J. J. Yang*, Resistive Switching Materials for Computing, Nature Review Materials, 5, 173-195 (2020) [First author]
2. Z. Wang?, C. Li?, P. Lin?, M. Rao, Y. Nie, W. Song, Q. Qiu, Y. Li, P. Yan, J. P. Strachan, N. Ge, N. McDonald, Q. Wu, M. Hu, H. Wu, R. S. Williams, Q. Xia*, and J. J. Yang*, In situ training of feedforward and recurrent convolutional memristor networks, Nature Machine Intelligence, 1, 434-442 (2019) [First author]
3. Z. Wang?, C. Li?, W. Song, M. Rao, D. Belkin, Y. Li, P. Yan, H. Jiang, P. Lin, M. Hu, J. P. Strachan, N. Ge, M. Barnell, Q. Wu, A. G. Barto, Q. Qiu, R. S. Williams, Q. Xia*, and J. J. Yang*, Reinforcement learning with analogue memristor arrays, Nature Electronics, 2, 115-124 (2019) [First author]
4. Z. Wang? , S. Joshi?(?equally contributed), S. Saveliev, W. Song, R. Midya, M. Rao, Y. Li, P. Yan, S. Asapu, Y. Zhuo, H. Jiang, P. Lin, C. Li, J. H. Yoon, N. K. Upadhyay, J. Zhang, M. Hu, J. P. Strachan, M. Barnell, Q. Wu, H. Wu, R. S. Williams*, Q. Xia*, and J. J. Yang*, Fully memristive neural networks for pattern classification with unsupervised learning, Nature Electronics, 1, 137-145 (2018) [First author]
5. Z. Wang?, S. Joshi?(?equally contributed), S. E Savel’ev, H. Jiang, R. Midya, P. Lin, M. Hu, N. Ge, J. P. Strachan, Z. Li, Q. Wu, M. Barnell, G.-L. Li, H. L Xin, R. S. Williams, Q. Xia, and J. J. Yang*, Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing, Nature Materials, 16, 101-108 (2017) [First author]