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 machine learning and neuromorphic computing based on novel computing-in-memory architectures. He has published papers as a corresponding or first author in journals such as Nature Reviews Materials, Nature Materials, Nature Electronics (4 papers), and Nature Machine Intelligence (2 papers), as well as conferences like DAC, ICCAD, and ICCV. His work has received nearly 16,000 citations on Google Scholar (h-index of 41) and has been featured in over 40 news outlets, including IEEE Spectrum, Scientific American, Science Daily, Phys.org, and ACM Communications.

Dr. Wang is 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: wangzr@sustech.edu.cn. 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

Research Interests

(Students with a background in machine learning, computer architecture, digital design, or physics/statistics are welcome to apply)

· Computing-in-memory architecture

· Hardware-software co-design based on emerging computing-in-memory architectures

· AI4S

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. 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]

3. S. Wang†, X. Chen†(†equally contributed), 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]

4. 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]

5. 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]

6. 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, ArXiv: 2307.00771 https://arxiv.org/abs/2307.00771 [Main corresponding author]

7. 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, ResearchSquare: 3967300 https://doi.org/10.21203/rs.3.rs-3967300/v1 [Main 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]