In 2013, I joined Bloomberg L.P. at the Financial Derivatives Division to develop systems that manages risks of collateralized trades.
Since 2015, I have been working for Google as software engineer.
Research
My main research interests are in complex networks theory and application and econophysics.
- Complex Networks:
Complex systems composed of many interacting entities can be represented, analyzed and better understood using a network representation, where the entities are represented by nodes and the interactions by links.
I am interested in developing new complex network theories as well as their applications, such as robustness, effective immunization strategies and optimal transport in real world networks.
- Econophysics:
Generally, it means using statistical physics concepts to better understand economic questions.
To me, as a physicist, I view economic systems as a collection of interacting units. This collection is complex; everything depends on everything else. Thus complex networks models and theories can be applied to study these economic systems very well, which enables physicists to quantitatively understand and even predict some economic and finance phenomena.
Selected Projects
Robustness of complex system under targeted attack [PDF]
When an initial failure of nodes occurs in interdependent networks, a cascade of failure between the networks occurs. Here we study the robustness of interdependent networks under targeted attack on high or low degree nodes. We introduce a general technique which maps the targeted-attack problem in interdependent networks to the random-attack problem in a transformed pair of interdependent networks. We find that when the highly connected nodes are protected and have lower probability to fail, in contrast to single scale-free (SF) networks where the percolation threshold p_c = 0, coupled SF networks are significantly more vulnerable with pc significantly larger than zero. The result implies that interdependent networks are difficult to defend by strategies such as protecting the high degree nodes that have been found useful to significantly improve robustness of single networks.
Complex network percolation under localized attack [PDF]
In many real-world scenarios, attacks are neither random nor hub-targeted, but localized, where a group of neighboring nodes in a network are attacked and fail. In this paper we develop a percolation framework to analytically and numerically study the robustness of complex networks against such localized attack. In particular, we investigate this robustness in Erdos-Renyi networks, random-regular networks, and scale-free networks. Our results provide insight into how to better protect networks, enhance cybersecurity, and facilitate the design of more robust infrastructures.
Identify the Most Influential Directors in US Corporate Governance Network [PDF]
We analyze the structure of the US corporate governance network for the 11-year period 1996-2006 based on director data from the Investor Responsibility Research Center director database, and we develop a centrality measure named the influence factor to estimate the influence of directors quantitatively. The US corporate governance network is a network of directors with nodes representing directors and links between two directors representing their service on common company boards. We assume that information flows in the network through information-sharing processes among linked directors. The influence factor assigned to a director is based on the level of information that a director obtains from the entire network. We find that, contrary to commonly accepted belief that directors of large companies, measured by market capitalization, are the most powerful, in some instances, the directors who are influential do not necessarily serve on boards of large companies. By applying our influence factor method to identify the influential people contained in the lists created by popular magazines such as Fortune, Networking World, and Treasury and Risk Management, we find that the influence factor method is consistently either the best or one of the two best methods in identifying powerful people compared to other general centrality measures that are used to denote the significance of a node in complex network theory.
Cascading Failures in Bi-partite Graphs: Model for Systemic Risk Propagation [PDF]
As economic entities become increasingly interconnected, a shock in a financial network can provoke significant cascading failures throughout the system. To study the systemic risk of financial systems, we create a bi-partite banking network model composed of banks and bank assets and propose a cascading failure model to describe the risk propagation process during crises. We empirically test the model with 2007 US commercial banks balance sheet data and compare the model prediction of the failed banks with the real failed banks after 2007. We find that our model efficiently identifies a significant portion of the actual failed banks reported by Federal Deposit Insurance Corporation. The results suggest that this model could be useful for systemic risk stress testing for financial systems. The model also identifies that commercial rather than residential real estate assets are major culprits for the failure of over 350 US commercial banks during 2008-2011.
Partial Correlation Analysis of Financial Market [PDF]
Financial markets exhibit systemic shifts and display non-equilibrium properties. To understand how risks propagate through the entire system, many studies have focused on understanding the synchronization in financial markets that is especially pronounced during periods of crisis. Previous work has focused on how variable j affects variable i, by averaging over all (i, k) pairs, thus quantifying how variable j affects the average correlation of i with all other variables. While this has provided important information that has been both investigated and statistically validated, our goal here is to present a more general and robust method to statistically pick the meaningful relationships without first averaging over all pairs. Unlike the previous work, in which the average influence of j on the correlation of i with all others was calculated, and then statistically validated, here, we first filter for validated links, and then average the influence. In order to achieve this, we expand the original methodology and use statistical validation methods to filter the significant links. This statistically validated selection process reveals signifi- cant influence relationships between different financial assets. This new methodology allows us to quantify the influence of different factors (e.g. economic sectors, other markets or macroeconomic factors) have on a given asset. The information generated by this methodology is applicable to such areas as risk management, portfolio optimization and financial contagion, and is valuable to both policy-makers and practitioners.Pulications
Google Scholar Page- S. Shao, X. Huang, H. E. Stanley and S. Havlin, Percolation of localized attack on complex networks , New Journal of Physics 17(2) 023049 (2015).
- X. Huang, Q. Li, J. Shie, S. Havlin and H. E. Stanley, Bipartite networks: increasing survival threshold leads to a change of from second order to first order phase transition.
- X. Huang, D. Kenett, I. Vodenska, S. Havlin and H. E. Stanley, Partial Correlation Analysis of Global Stock Markets, Quantitative Finance 15(4) 569-578 (2015).
- S. Shao, X. Huang, H. E. Stanley and S. Havlin, Robustness of Partially Interdependent Network Formed of Clustered Networks, Physical Review E 89(3) 032812 (2015).
- X. Huang, S. Shao, H. Wang, S. V. Buldyrev, H. E. Stanley and S. Havlin, Robustness of Interdependent Clustered Networks, Europhysics Letters 101, 18002 (2013).
- X. Huang, I. Vodenska, S. Havlin and H. E. Stanley, Cascading Failures in Bipartite Networks: Model for Systemic Risk Propagation, Nature Scientific Reports 3, 1219 (2013).
- X. Huang, J. Gao, S. V. Buldyrev, S. Havlin, H. E. Stanley, Robustness of Interdependent Networks Under Targeted Attack, Phys. Rev. E 83, 065101(R) (2011)
- X. Huang, I. Vodenska, F. Wang, S. Havlin, H. E. Stanley, Identify the Most Influential Directors in US Corporate Governance Network, Phys. Rev. E 84, 046101 (2011)
Teaching
PY105, PY106, PY211, PY212Useful Resources
- Links to Useful Data
- NetworkX (Network Graph Tool)
- Ubuntu is good for you
Updated Jul 23, 2016