Professor Ning Zhong,  PhD

Faculty of Engineering
Maebashi Institute of Technology, Japan

E-mail: zhong at maebashi-it dot ac dot jp



Honors and Activities

Research Interests

Selected Publications



Ning Zhong obtained his Ph.D. from the University of Tokyo. He currently holds positions as the Co-director & Co-chairman of the Web Intelligence Consortium (WIC), CEO & Chief Scientist of the Web Intelligence Lab, Professor Emeritus/Visiting Professor, and previously served as a professor in the Department of Life Science and Informatics at Maebashi Institute of Technology, Japan. Prof. Zhong is a foreign fellow of the Engineering Academy of Japan (EAJ).

Prof. Zhong's present research interests include Web Intelligence (WI), Brain Informatics (BI), Data Mining, Granular Computing, and Intelligent Information Systems. In 2000 and 2004, Zhong and colleagues introduced WI and BI as new research directions, respectively. Currently, he is focusing on "WI meets BI" research with three aspects: (1) systematic investigations for complex brain science problems; (2) BI studies based on WI research needs; and (3) new information technologies for supporting systematic brain science studies. The synergy between WI and BI advances our ways of analyzing and understanding of data, information, knowledge, wisdom, as well as their interrelationships, organizations, and creation processes, to achieve human-level Web intelligence reality. In 2010, Zhong and colleagues extended such a vision to develop Wisdom Web of Things (W2T) as a holistic framework for computing and intelligence in the big data era. Recently Zhong and colleagues have been working on brain big data based wisdom service project, in which the fundamental issues include how brain informatics based big data interacts in the social-cyber-physical space of the W2T and how to realize human-level collective intelligence as a big data sharing mind, a harmonized collectivity of consciousness on the W2T that uses brain-inspired intelligent technologies to provide wisdom services.

Representative Publications:

    H. Kuai, J. Chen, X. Tao, L. Cai, K. Imamura, H. Matsumoto, P. Liang, N. Zhong. Never-Ending Learning for Explainable Brain Computing, Advanced Science, 11 April 2024,, Wiley-VCH GmbH

    Q. Zuo, Y. Shen, N. Zhong, C L Philip Chen, B. Lei, S. Wang. Alzheimer's Disease Prediction via Brain Structural-Functional Deep Fusing Network. IEEE Trans Neural Syst Rehabil Eng. 2023:31:4601-4612. doi: 10.1109/TNSRE.2023.3333952.

    Q. Zuo, N. Zhong, Y. Pan, H. Wu, B. Lei and S. Wang. Brain Structure-Function Fusing Representation Learning Using Adversarial Decomposed-VAE for Analyzing MCI, in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 4017-4028, 2023, doi: 10.1109/TNSRE.2023.3323432.

    L. Manubens-Gil, Z. Zhou, ..., Y. Guo, N. Zhong, G. Tourassi, S. Hill, M. Hawrylycz, C. Koch, E. Meijering, G.A. Ascoli, and H. Peng (corresponding author). BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy da-tasets. Nature Methods, 2023.

    Y. Cao, H. Kuai, P. Liang, J.S. Pan, J. Yan, and N. Zhong (corresponding author). BNLoop-GAN: A Multi-Loop Generative Adversarial Model on Brain Network Learning to Classify Alzheimer's Disease. Frontiers in Neuroscience, 17:1202382, 2023. DOI: 10.3389/fn ins.2023.1202382

    H. Kuai, X. Tao, and N. Zhong (corresponding author). Thinking space generation using context-enhanced knowledge fusion for systematic brain computing. Web Intelligence, Pre-press 2023. DOI:10.3233/WEB-220089

    J. Xu, J. Huang, J. Yang, N. Zhong. M2GCF: A multi-mixing strategy for graph neural network based collaborative filtering. Web Intelligence, Pre-press 2022. DOI:10.3233/WEB-220054

    X. Zhang, Y. Yang, H. Kuai, J. Chen, J. Huang, P. Liang, N. Zhong (corresponding author). Systematic Fusion of Multi Source Cognitive Networks with Graph Learning - A Study on Fronto-Parietal Network. Frontiers in Neuroscience, 16:866734, 2022. DOI: 10.3389/fnins.2022.866734

    H. Kuai, X. Tao, N. Zhong. Web Intelligence Meets Brain Informatics: Towards the Future of Artificial Intelligence in the Connected World, World Wide Web, Springer, 2022. DOI: 10.1007/s11280-022-01030-5

    H. Kuai, N. Zhong (corresponding author), J. Chen, Y. Yang, X. Zhang, P. Liang, K. Imamura, L. Ma, and H. Wang. Multi-source brain computing with systematic fusion for smart health. Information Fusion, an International Journal, Volume 75, 2021, Pages 150-167, Elsevier. DOI:10.1016/j.inffus.2021.03.009

    H. Kuai and N. Zhong (corresponding author), The extensible Data-Brain model: Architecture, applications and directions. Journal of Computational Science, Volume 46, October 2020, 101103. Elsevier. Part of special issue: SI: 20 Years of the International Conference on Computational Science: Keynotes, Edited by Sergey V. Kovalchuk, Mike Lees, Valeria Krzhizhanovskaya, Jack Dongarra, Peter Sloot. DOI: 10.1016/j.jocs.2020.101103.

    Z. Wan, J. Huang, H. Zhang, H. Zhou, J. Yang and N. Zhong (corresponding author), HybridEEGNet: A Convolutional Neural Network for EEG Feature Learning and Depression Discrimination. IEEE Access, 2020.2, DOI: 10.1109/ACCESS.2020.2971656

    H. Kuai, X. Zhang, Y. Yang, J. Chen, B. Shi, N. Zhong (corresponding author), THINKING-LOOP: The Semantic Vector Driven Closed-Loop Model for Brain Computing. IEEE Access, 2020.1, DOI: 10.1109/ACCESS.2019.2963070

    J. Chen, N. Wang, Y. Deng, H. Zhong, J. Han, Y. Li, Z. Wan, T. Kotake, D. Wang, and N. Zhong, Wisdom as a Service for Mental Health Care. IEEE Trans. Cloud Comput. 8(2): 539-552 (2020)

    Y. Wang, Q. Li, L. Liu, Z. Zhou, Z. Ruan, L. Kong, Y. Li, Y. Wang, N. Zhong, ..., H. Peng (corresponding author). TeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain. Nature Communications, 2019. 10. DOI:10.1038_s41467-019-11443-y

    N. Zhongi, J. Liu, Y. Shi, and Y.Y. Yao. An interview with Professor Raj Reddy on Wed Intelligence (WI) and Computational Social Science (CSS). Web Intelligence, vol. 16, no. 3, pp. 143-146, 2018.

    J. Yang, M. Hao, X. Liu, Z. Wan, N. Zhong (corresponding author), H. Peng. FMST: an Automatic Neuron Tracing Method Based on Fast Marching and Minimum Spanning Tree. Neuroinformatics, 2018. 17. DOI:10.1007/s12021-018-9392-y

    Y. Yang, N. Zhong (corresponding author), K. Friston, K. Imamura, S. Lu, M. Li, H. Zhou, H. Wang, K. Li, and B. Hu. The Functional Architectures of Addition and Subtraction: Network Discovery Using fMRI and DCM. Human Brain Mapping, Wiley, 2017. 38. DOI: 10.1002/hbm.23585

    Z. Wan, Y. He, M. Hao, J. Yang, and N. Zhong (corresponding author). M-AMST: An Automatic 3D Neuron Tracing Method Based on Mean Shift and Adapted Minimum Spanning Tree. BMC Bioinformatics, 2017, 18:197. DOI 10.1186/s12859-017-1597-9

    N. Zhong, J. Ma, J. Liu, R. Huang, X. Tao (Eds.) Wisdom Web of Things, Springer, 2016.

    Y. Yang, N. Zhong (corresponding author), K. Imamura, S. Lu, M. Li, H. Zhou, H. Li, X. Yang, Z. Wan, G. Wang, B. Hu, K. Li. Task and Resting-State fMRI Reveal Altered Salience Responses to Positive Stimuli in Patients with Major Depressive Disorder. PLoS One, 18 May 2016, 10.1371/journal.pone.0155092.

    M. Li, N. Zhong (corresponding author), S. Lu, G. Wang, L. Feng, and B. Hu. Cognitive Behavioral Performance of Untreated Depressed Patients with Mild Depressive Symptoms. PLoS One, 5 January 2016, 10.1371/journal.pone.0146356.

    N. Zhong, Y. Yang, K. Imamura, S. Lu, M. Li, H. Zhou, G. Wang, and K. Li. Self-regulation of Aversive Emotion: A Dynamic Causal Model. Advances in Computational Psychophysiology, Science Supplement, 2 October 2015, 25-27.

    N. Zhong, S.S. Yau, J. Ma, S. Shimojo, M. Just, B. Hu, G. Wang, K. Oiwa, and Y. Anzai. Brain Informatics-Based Big Data and the Wisdom Web of Things. IEEE Intelligent Systems, 2015, 30(5): 2-7.

    J. Chen, J.H. Ma, N. Zhong (corresponding author), Y.Y. Yao, J. Liu, R.H. Huang, W. Li, Z. Huang, Y. Gao, and J. Cao. WaaS - Wisdom as a Service. IEEE Intelligent Systems, 2014, 29(6): 40-47.

    N. Zhong, J.H. Ma, R.H. Huang, J.M. Liu, Y.Y. Yao, Y.X. Zhang, and J.H. Chen. Research Challenges and Perspectives on Wisdom Web of Things (W2T). Journal of Supercomputing, Springer, 2013, 64(3): 862-882.

    N. Zhong and J. Chen. Constructing a New-style Conceptual Model of Brain Data for Systematic Brain Informatics. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(12): 2127-2142.

    N. Zhong, Y. Li, and S.T. Wu. Effective Pattern Discovery for Text Mining. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(1): 30-44.

    Z. Wang, J. Liu, N. Zhong (corresponding author), Y. Qin, H. Zhou, and K. Li. Changes in the Brain Intrinsic Organization in Both On-Task State and Post-Task Resting State, NeuroImage, Elsevier, 2012, 62: 394-407.

    X. Jia, P. Liang, J. Lu, Y. Yang, N. Zhong (corresponding author), and K. Li. Common and Dissociable Neural Correlates Associated with Component Processes of Inductive Reasoning. NeuroImage, Elsevier, 2011, 56: 2292-2299.

    N. Zhong, J.M. Bradshaw, J. Liu, and J.G. Taylor. Brain Informatics. IEEE Intelligent Systems, 2011, 26(5): 16-21.

    N. Zhong and S. Motomura. Agent-Enriched Data Mining: A Case Study in Brain Informatics. IEEE Intelligent Systems, 2009, 24(3): 38-45.

    N. Zhong, J. Liu, and Y.Y. Yao. Web Intelligence (WI). The Encyclopedia of Computer Science and Engineering, Wiley, 2009, 5: 3062-3072.

    N. Zhong, J. Liu, and Y.Y. Yao. Envisioning Intelligent Information Technologies through the Prism of Web Intelligence. Communications of the ACM, 2007, 50(3): 89-94.

    N. Zhong, J. Liu, Y.Y. Yao, J. Wu, S. Lu, Y. Qin, K. Li, and B. Wah. Web Intelligence Meets Brain Informatics. N. Zhong et al (eds.) Web Intelligence Meets Brain Informatics, State-of-the-Art-Survey, Springer LNCS 4845, 2007, 1-31.

    N. Zhong, Y.Y. Yao, and M. Ohshima. Peculiarity Oriented Multi-Database Mining. IEEE Transactions on Knowledge and Data Engineering, 2003, 15(4): 952-960.

    N. Zhong, J. Liu, and Y.Y. Yao. In Search of the Wisdom Web. IEEE Computer, 2002, 35(11): 27-31.

Zhong's Lab.
WI Consortium
Contact Information
Office: Faculty of Engineering
Maebashi Institute of Technology

This page has been accessed times since 3 December 2000.
Last updated: 8 June 2016