Dr Liang Tian is currently an Assistant Professor in the Department of Physics at Hong Kong Baptist University (HKBU). He also serves as an academic member in the State Key Laboratory of Environmental and Biological Analysis (HKBU) and the Institute of Computational and Theoretical Studies, and an associated member in the Shenzhen JL Computational Science and Applied Research Institute. Before joining HKBU, he was a postdoctoral fellow in the Harvard Medical School at Harvard University and a research fellow in the Channing Division of Network Medicine at Brigham and Women’s Hospital (Boston, USA). Dr Tian’s group performs cutting-edge interdisciplinary research on Complex Systems, Statistical Physics, and Biophysics by using various analytical, numerical, modelling, simulation, and data-mining and machine-learning techniques. The main topics include Human Microbiome and Community Ecology, Biological Big-data and Machine Learning, Complex Network: Structure and Dynamics, Data-mining in Traditional Chinese Medicine, Epidemiological Modelling and so on.

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.