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TIAN, Liang

TIAN, Liang, 田亮博士

Assistant Professor

B.Sc., Ph.D., Nanjing University of Aeronautics and Astronautics, Nanjing, China
Postdoctoral, Harvard Medical School, Boston, USA
Postdoctoral, Brigham and Women’s Hospital, Boston, USA

Rm. T904

(852) 3411-7030

Current Research Interests

Dr. Tian performs theoretical and empirical research in the fields of Complex Systems, Statistical Physics, and System Biology by using various analytical, numerical, simulation, and data mining and machine learning techniques. The main topics include phase transition and critical phenomena, complex network, human microbiome and community ecology, and so on.


Current projects include:

Human Microbiome and Community Ecology

Human-associated microbes form a very complex and dynamic ecosystem, which can be altered by drastic diet change, medical interventions, and many other factors. The alterability of our microbiome offers a promising future for a variety of microbiome-based therapies such as ingesting probiotics or prebiotics, and fecal microbiota transplantation, in treating diseases associated with disrupted microbiota. Despite successful cases under each strategy, we still lack sufficient understanding of which strategy works best for a given individual, and whether there are long-term safety issues. Indeed, the complex topology and dynamics of the ecological network underlying the human gut microbiota render quantitative study of effects of external interventions extremely difficult. The future of microbiome-based therapies will be bright only if we fully understand the structure and dynamics of our gut microbial ecosystems. Our long-term objective is to construct a modeling framework to better capture the community ecology and dynamics to inform microbiome-based therapies.


Biological Big-Data and Machine Learning

The exponential growth of the amount of biological data available today prompts us to adopt and develop machine techniques to transform all these heterogeneous data into biological knowledge and testable models. We are generally interested in multi-dimensional biological data analysis using various machine learning techniques, such as hidden Markov modeling, network-based clustering, Bayesian network, consensus clustering, echo state networks, and so on. Recently, we focus on exploring the impact of the structure of artificial neural networks on their performance.


Complex Networks: Structure and Dynamics

We are interested in the intricate interplay between the structure and dynamics of complex networks. In particular, using tools from statistical physics and graph theory, we studied various percolation transitions on complex networks, revealing their implications in dynamical processes on networked systems. We are also interested in application and development of modern statistical physics techniques and methodologies for data processing and network reconstruction, such as spectral methods in time-series data analysis and dimension reduction. Progress in this direction will improve statistical physics models across different time scales, extract universal properties, and explore new ideas towards relationships between network structure and dynamics, providing the theoretical underpinning for the other projects.

Selected Publications

  1. Calibrated Intervention and Containment of the COVID-19 Pandemic, Liang Tian, Xuefei Li, Fei Qi, Qian-Yuan Tang, Viola Tang, Jiang Liu, Zhiyuan Li, Xingye Cheng, Xuanxuan Li, Yingchen Shi, Haiguang Liu, Lei-Han Tang (2020).
  2. Structural vulnerability of quantum networks, AK Wu, L Tian, BC Coutinho, Y Omar, YY Liu Physical Review A 101 (5), 052315 (2020)
  3. ACCELERATING THE EMERGENCE OF ORDER IN SWARMING SYSTEMS, Y XIAO, C SONG, L TIAN, YYU LIU Advances in Complex Systems, Advances in Complex Systems 22, 1950015 (2019)
  4. Sonawane AR, Tian L, Chu CY, Qiu X, Wang L, Holden-Wiltse J, Grier A, Gill SR, Caserta MT, Falsey AR, Topham DJ. Microbiome-transcriptome interactions related to severity of respiratory syncytial virus infection. Scientific reports. 2019 Sep 25;9(1):1-4.
  5. Pan Li, Ting Zhang, Yandong Xiao, Liang Tian, Bota Cui, Guozhong Ji, Yang-Yu Liu, Faming, Zhang, Timing for the second fecal microbiota transplantation to maintain the long-term benefit from the first treatment for Crohn’s disease, Applied Microbiology and Biotechnology, 103, 349 (2019).
  6.  Ang-kun Wu, Liang Tian, Yang-Yu Liu, Bridges in Complex Networks, Phys. Rev. E, 97, 012307 (2018).
  7.  Liang Tian, Amir Bashan, Da-Ning Shi, and Yang-Yu Liu, Articulation points in complex network, Nature Communications, 8 (2017).
  8. Liuhua Zhu, Liang Tian, and Da-Ning Shi, Criterion for the emergence of explosive synchronization transitions in networks of phase oscillators, Phys. Rev. E, 88, 042921 (2013).
  9. Liang Tian, Hui Ma, Wen-An Guo, and Lei-Han Tang, Phase transitions of the q-state Potts model on multiply-laced Sierpinski gaskets, Euro. Phys. J. B, 86, 179 (2013).
  10. Liang Tian and Da-Ning Shi, The nature of explosive percolation phase transition, Phys. Lett. A, 376, 286 (2012).
  11. Liang Tian and Da-Ning Shi, Scaling of disordered recursive scale-free networks, Europhys. Lett., 84, 58001 (2008).
  12.  Liang Tian, Chen-Ping Zhu, Da-Ning Shi, Zhi-Ming Gu, and Tao Zhou, Universal scaling behavior of clustering coefficient induced by deactivation mechanism, Phys. Rev. E, 74, 046103 (2006).