Jack Murdoch Moore

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Jack Murdoch Moore

Contact

Jack Murdoch Moore
School of Physics Science and Engineering
Tongji University
67 Chifeng Road, Yangpu
Shanghai, China

Email: jackmoore AT tongji DOT edu DOT cn
Telephone: +86 (021) 65983380

Biography

I am an associate professor at the School of Physics Science and Engineering, Tongji University, Shanghai, China. I received my B.Sc. and Ph.D. degree from the University of Western Australia in 2013 and 2018 respectively. I grew up in Rockingham, Western Australia, a city whose neighbouring islands host penguins and sea lions.

I am interested in statistical physics, complex systems, network science, nonlinear time series analysis, and Chinese culture. My Chinese name is 墨龙明 (Mò Lóngmíng), and I am usually called Jack or 小明 (Xiǎomíng).

Publications

Google Scholar profile

  1. J. M. Moore, M. Small, G. Yan, H. Yang, C. Gu, H. Wang, “Network spreading from network dimension,” Physical Review Letters, vol. 132, p. 237401, 2024.
  2. X. Ru, X.-Y. Zhang, Z. Liu, J. M. Moore*, G. Yan*, “Attentive Transfer Entropy to Exploit Transient Emergence of Coupling Effect,” in Advances in Neural Information Processing Systems, (Curran Associates, Inc., New Orleans), p. 171, 2023.
  3. X. Ru, J. M. Moore, X.-Y. Zhang, Y. Zeng, and G. Yan, “Inferring patient zero on temporal networks via graph neural networks,” in Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans (AAAI Press, Washington, DC), p. 9632, 2023.
  4. J. M. Moore, X. Zhang, G. Yan*, J. M. Moore*, “Foresight and relaxation enable efficient control of nonlinear complex systems,” Physical Review Research, vol. 5, no. 3, p. 033138, 2023.
  5. J. M. Moore, H. Wang, M. Small, G. Yan*, H. Yang, and C. Gu, “Correlation dimension in empirical networks,” Physical Review E, vol. 107, no. 3, p. 034310, 2023.
  6. X.-J. Zhang, J. M. Moore, G. Yan*, X. Li*, “Universal structural patterns in sparse recurrent neural networks,” Communications Physics, vol. 6, no. 1, p. 243, 2023.
  7. X. Ru, J. M. Moore, X. Y. Zhang, Y. Zeng, and G. Yan*, “Inferring Patient Zero on Temporal Networks via Graph Neural Networks,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 8, p. 9632, 2023.
  8. P. Wang, C. Gu*, H. Yang, H. Wang, and J. M. Moore, “Characterizing systems by multi-scale structural complexity,” Physica A: Statistical Mechanics and its Applications, vol. 609, p. 128358, 2023.
  9. L. Liu, S. Chen*, M. Small, J. M. Moore, K. Shang, “Global stability and optimal control of epidemics in heterogeneously structured populations exhibiting adaptive behavior,” Communications in Nonlinear Science and Numerical Simulation vol. 126, p. 107500, 2023.
  10. J. M. Moore, G. Yan*, and E. G. Altmann, “Nonparametric power-law surrogates,” Physical Review X, vol. 12, p. 021056, 2022.
  11. H. Wang, J. M. Moore*, M. Small, J. Wang, H. Yang, and C. Gu, “Epidemic dynamics on higher-dimensional small world networks,” Applied Mathematics and Computation, vol. 421, p. 126911, 2022.
  12. H. Wang, Z. Du*, J. M. Moore*, H. Yang, and C. Gu, “Causal networks reveal the response of Chinese stocks to modern crises,” Information Sciences, vol. 609, p. 1670, 2022.
  13. L. Cui, J. M. Moore*, “Causal network reconstruction from nonlinear time series: A comparative study,” International Journal of Modern Physics C, vol. 32, no. 4, p. 1, 2021.
  14. J. M. Moore*, M. Small, and G. Yan, “Inclusivity enhances robustness and efficiency of social networks,” Physica A: Statistical Mechanics and its Applications, vol. 563, p. 125490, 2021.
  15. H. Wang, J. M. Moore, J. Wang*, and M. Small, “The distinct roles of initial transmission and retransmission in the persistence of knowledge in complex networks,” Applied Mathematics and Computation, vol. 392, p. 125730, 2021.
  16. D.C. Corrêa, J. M. Moore*, T. Jüngling, and M. Small, “Constrained Markov order surrogates,” Physica D: Nonlinear Phenomena, vol. 406, p. 132437, 2020.
  17. J. M. Moore*, D. M. Walker, and G. Yan, “Mean local autocovariance provides robust and versatile choice of delay for reconstruction using frequently sampled flowlike data,” Physical Review E, vol. 101, no. 1, p. 012214, 2020.
  18. K. K Shang*, B. Yang, J. M. Moore, Q. Ji, and M. Small, “Growing networks with communities: A distributive link model,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 30, no. 4, p. 041101, 2020.
  19. X. Peng, M. Small, Y. Zhao*, and J. M. Moore, “Detecting and predicting tipping points,” International Journal of Bifurcation and Chaos, vol. 29, no. 8, p. 1930022, 2019.
  20. H. Wang, J. Wang*, M. Small, and J. M. Moore, “Review mechanism promotes knowledge transmission in complex networks,” Applied Mathematics and Computation, vol. 340, p. 113, 2019.
  21. J. M. Moore, D.C. Corrêa*, and M. Small, “Is Bach’s brain a Markov chain? Recurrence quantification to assess Markov order for short, symbolic, musical compositions,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 28, no. 8, p. 085715, 2018.
  22. J. M. Moore* and M. Small, “Estimating dynamical dimensions from noisy observations,” Information Sciences, vol. 462, p. 55, 2018.
  23. J. Moore*, A. Karrech, M. Small, E. Veveakis, and K. Regenauer-Lieb, “Dissipative propagation of pressure waves along the slip-lines of yielding material,” International Journal of Engineering Science, vol. 107, p. 149, 2016.
  24. J. M. Moore*, A. Karrech, and M. Small, “Improvements to local projective noise reduction through higher order and multiscale refinements,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 25, no. 6, p. 063114, 2015.

Projects

  1. J. M. Moore, “Uncovering and evaluating the empirical laws of complex systems,” Ministry of Science and Technology of China, Foreign Young Talents Program, 2023.
  2. J. M. Moore, “Causal network inference for realistically nonlinear and non-separable complex systems,” National Natural Science Fund of China, Research Fund for International Young Scientists, grant no. 12150410309, 2022.