Name: Zi-Gang Huang

Country: China


  • School of Life Science & Technology, Xi’an Jiao Tong University, Xi’an, 710049, P. R. China,  
  • Institute of Computational Physics and Complex Systems, Lanzhou University, Lanzhou, 730000, P. R. China.

Phone no: +86-17710450995

Email: Send an Email


  • School of Life Science & Technology, Xi’an Jiao Tong University, Xi’an, 710049, P. R. China,  
  • Institute of Computational Physics and Complex Systems, Lanzhou University, Lanzhou, 730000, P. R. China.

Research Interests:

  1. Big data analysis and modeling of biological, ecological and social systems: Causality detection, resilience, robustness, optimization and nonlinear control in complex systems based on data from biosystems and ecosystems; Inverse problem of the structure and dynamics of the static multi-layer large-scale systems, detection of mutual force among self-driven agents in physical space; Online collective behavior and extreme events, evolution of human interests, human activity recognition based on sensor data.
  2. Nonequilibrium statistical physics and nonlinear dynamics in complex network systems: Self-organized processes, phase transition, critical behavior, flux fluctuation in the networked real systems which can be modeled by the excitable media, coupled oscillators, minority game, traffic dynamics, and epidemic spreading.
  3. The basic theoretical research of machine learning and big-data analysis: Data fusion and analyzing techniques for multi-dimensional and multi-scale data sets; Self-organized critical behavior in neural networks and the flexible control of information processing in brain-inspired computing.


  1. J.-J. Jiang, Z.-G. Huang, T. P. Seager, W. Lin, C. Grebogi, A. Hastings, and Y.-C. Lai, ``Predicting tipping points in mutualistic networks through dimension reduction,''Proceedings of the National Academy of Sciences (USA), accepted. 


  1. S. Wang, G. Su, Z.-G. Huang, M. Liu, L. Liu, Towards Complex Activity Recognition Using a Bayesian Network-Based Probabilistic Generative Framework, Pattern Recognition 10, 1016 (2017). 


  1. L.-Z. Wang, R. Su, Z.-G. Huang, X. Wang, W.-X. Wang, C. Grebogi, and Y.-C. Lai, Control and controllability of nonlinear dynamical networks: a geometrical approach, Nature Communications 7: 11323 doi 10.1038/ncomms11323 (2016). 
  2. J.-Q. Zhang, Z.-G. Huang*, Z.-X. Wu, R. Su, and Y.-C. Lai, Controlling herding in minority game systems, Scientific Reports 6, 20925; doi: 10.1038/srep20925 (2016). 
  3. J.-J. Jiang, Z.-G. Huang, L. Huang, H. Liu, and Y.-C. Lai*, Directed dynamical influence is more detectable with noise, Scientific Reports 6, 24088; doi 10.1038/srep24088 (2016). 
  4. L. Liu, S. Wang, Y. Peng, Z.-G. Huang, M. Liu, B. Hu, Mining intricate temporal rules for recognizing complex activities of daily living under uncertainty, Pattern Recognition 60, 1015 (2016). 2015
  5. S.-P. Zhang, Z.-G. Huang*, J.-Q. Dong, D. Eisenberg, T. P. Seager, and Y.-C. Lai*, Optimization and resilience of complex supply-demand networks, New J. Phys. 17 063029 (2015) (*Corresponding Author). 
  6. Y.-Z. Chen, Z.-G. Huang*, S.-H. Xu and Y.-C. Lai*, Spatiotemporal patterns and predictability of cyberattacks, PloS ONE 10(5) e0124472 (2015) (*Corresponding Author). 
  7. Q. Chen, Z.-G. Huang*, Y. Wang, and Y.-C. Lai*, Multiagent model and mean field theory of complex auction dynamics, New J. Phys. 17 093003 (2015) (*Corresponding Author)
  8. W.-C. Yang, Z.-G. Huang, X.-G. Wang, L. Huang, L. Yang, and Y.-C. Lai, Complex behavior of chaotic synchronization under dual coupling channels, New J. Phys.17, 023055 (2015). 
  9. N. Yao, Z.-G. Huang, C. Grebogi, and Y.-C. Lai, Emergence of multicluster chimera states, Scientific Reports 5, 12988; 10.1038/srep12988 (2015). 
  10. Y.-Z. Chen, Z.-G. Huang, H.-F. Zhang, D. Eisenberg, T. P. Seager, and Y.-C. Lai, Extreme events in multilayer, interdependent complex networks and control, Scientific Reports 5, 17277; doi 10.1038/srep17277 (2015). 
  11. L. Liu, Y. Peng, M. Liu, Z.-G. Huang, Sensor-based Human Activity Recognition System with A Multilayered Model Using Time Series Shapelets, Knowledge-Based Systems 90, 138–152 (2015). 
  12. L. Liu, Z.-G. Huang, Y. Peng, and M. Liu, "A Hierarchical Pachinko Allocation Model for Social Sentiment Mining." Knowledge Science, Engineering and Management (KSEM). Springer International Publishing, 299-311 (2015).

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