Email: phyfeng@nus.edu.sg
Current Research
- Dr Feng Ling specializes in complex systems research, both in theoretical foundations and practical problems, ranging from the statistical physics behind deep learning to social systems. In his theoretical studies, he looks at the complexity and phase transition phenomena in artificial neural networks, trying to uncover the working principle of deep learning. He has also been working on the various percolation transition phenomenon in inter-dependent complex networks to develop frameworks to construct different phases in spreading phenomena. In the application of complexity theories, he has applied complex network theories to various social and economic networks ranging from social media, financial networks and blockchain networks, building predictive algorithms in synergy with various machine learning algorithms. On the practical side, he has also been actively working on various large scale industry projects dealing with urban complexity, covering transport, land use and energy infrastructure.
- Feng L., Zhang L. & Lai C. H. Optimal Machine Intelligence Near the Edge of Chaos. arXiv e-prints, arXiv-1909. (2020)
- Sun J, Feng L, Xie J et al. Revealing the predictability of intrinsic structure in complex networks. Nature Communications 11, 574 (2020)
- Chen X, Zhou T, Feng L, Liang J, Liljeros F, Havlin S, Hu Y. Nontrivial resource requirement in the early stage for containment of epidemics. Physical review E. Sep 23;100(3):032310 (2019)
- Aspembitova A, Feng L, Melnikov V, Chew LY. Fitness preferential attachment as a driving mechanism in bitcoin transaction network. PloS one, (2019)
- Hu Y, Ji S, Jin Y, Feng L, Stanley H E & Havlin S. Local structure can identify and quantify influential global spreaders in large scale social networks. Proceedings of the National Academy of Sciences (PNAS), 115, 29 (2018)
- Ren AH, Feng L, Cheong SA, Goh RS. Optimal fee structure for efficient lightning networks. In2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS) pp. 980-985, (2018)
- Zhang X, Feng L, Berman Y, Hu N, Stanley HE. Exacerbated vulnerability of coupled socio-economic risk in complex networks. EPL (Europhysics Letters).(2016)
- Feng L, Hu Y, Li B, Stanley H. E, Havlin S, Braunstein L A., Competing for Attention in Social Media under Information Overload Conditions, PLoS One, PLoS ONE 10(7): e0126090, (2015)
- Feng L, Monterola C P, Hu Y, The simplified self-consistent probabilities method for percolation and its application to interdependent networks, New Journal of Physics 17.6 (2015)
- Feng L, Li B, Podobnik B, Preis T, Stanley H E, Linking Agent-based Models and Stochastic Models of Financial Markets. Proceedings of the National Academy of Sciences (PNAS), 109(22), (2012)
- Manager of Complex System Group at the Institute of High Performance Computing, A*STAR Singapore.
Complexity science of deep learning. Biological brains have been found to work near some critical state, and some studies have demonstrated through simulations that certain critical state possess the ability to transfer high amount of information. By examining the modern deep neural networks, we have uncovered the theoretical principle behind this phenomenon, and further pinpointed the critical state to be between periodic cycles and chaos, universal across different dynamical systems. Such finding leads to new potential avenues to design and train neural networks, as well as extracting their explainability. (Feng L., Zhang L. & Lai C. H. Optimal Machine Intelligence Near the Edge of Chaos. arXiv e-prints, arXiv-1909, 2020)
Percolation in complex networks. Many real world systems can be represented as networks, over which interactions occur. Such systems usually, if not always exhibits phase transition behaviors like the melting of ice to water. One thing in common in many of such systems are the spreading events, ranging from infectious disease over human contact network to information over online social media. Typically there are two distinct phases of the spreading outcomes: limited local spread vs. global widespread. In different problems, the desirable phase can be the former or the later. Through percolation theory in statistical mechanics, we have developed frameworks and tools to understand and quantify such phase transitions in various social economic systems, and developed various algorithms to efficiently maximize or mitigate spreading at the systemic level. (Hu Y, Ji S, Jin Y, Feng L, Stanley H E & Havlin S. Local structure can identify and quantify influential global spreaders in large scale social networks. Proceedings of the National Academy of Sciences (PNAS), 115, 29, 2018)
