About Me

I am currently a post-doctoral research scientist working with Prof. Jeffrey Shaman in the Department of Environmental Health Sciences at Mailman School of Public Health, Columbia University. My research interest is the spreading dynamics in interdisciplinary complex systems, with a main focus on infectious disease spread and information diffusion. Using dynamical modeling and Bayesian data assimilation techniques, I work on simulation, inference and forecast of the seasonal and spatial transmission of various infectious diseases, including influenza, dengue fever and antibiotic-resistant pathogens, at both population and individual levels. In parallel, I also use theories in statistical physics and big-data analyses in real-world social platforms to develop and validate practical methods to identify influential spreaders in diverse network structures. In a more general scope, I work towards the development of novel computational methods to address significant problems in biological and social sciences.

Latest News

  • 12/14/2017 Our paper entitled "Dynamic range maximization in excitable networks" has been accepted for publication in Chaos. This is a collaborative work with Dr. Renquan Zhang at Dalian University of Technology in Dalian, China. In this work, we explored the role of non-backtracking matrix in determining the dynamic range of excitable networks, a universal mechanistic model of sensory neural networks. An efficient algorithm for dynamic range maximization was also devised and validated in this work.
  • 12/11/2017 I am invited to give a talk on the inference of nosocomial transmission of antibiotic resistant pathogens in the School of International and Public Affairs at Columbia University.
  • 11/29/2017 Eight members of Shaman group attend the EPIDEMICS6 Sixth International Conference on Infectious Disease Dynamics in Sitges, Spain. I give a talk on "Forecasting the spatial spread of influenza in the United States".
  • 11/20/2017 I give a talk "Forecast and Inference of Infectious Disease Spread using Network Models" in the EHS deparment seminar.
  • 10/13/2017 Our paper "Counteracting structural errors in ensemble forecast of influenza outbreaks" is published in Nature Communications.
  • 09/01/2017 A collaboration work with research teams in Beihang University entilted "Promoting information diffusion through interlayer recovery processes in multiplex networks" is published in Physical Review E. In this work, we explored the coupling dynamics of a modified rumor diffusion model in multiplex networks with interlayer recovery processes. Our work reveals that the introduction of interlayer recovery can enhance the information diffusion process, for which the optimal coupling strength can be calculated numerically.
  • 07/31/2017 Our paper "Counteracting structural errors in ensemble forecast of influenza outbreaks" is accepted by Nature Communications. In this work, we inspect the error growth of a compartmental influenza model and develop a new forecast approach that combines dynamical error correction and statistical filtering techniques. In retrospective forecasts of historical influenza outbreaks for 95 U.S. cities from 2003 to 2014, overall forecast accuracy for outbreak peak timing, peak intensity and attack rate, are substantially improved for predicted lead times up to 10 weeks. This error growth correction method was used in the CDC 2016-2017 season flu challenge and improved the forecast accuracy.
  • 06/16/2017 I visit Dalian University of Technology in Dalian China, and give a talk "Finding influential spreaders in cascading processes in complex networks".
  • 05/23/2017 Twelve members of Shaman Group attend the MIDAS annual meeting in Atlanta, GA. I give an oral presentation "Forecasting the spatial transmission of influenza" at the general session.
  • 03/28/2017 Our paper "Efficient collective influence maximization in cascading processes with first-order transitions" is published in Scientific Reports. We developed an efficient algorithm that could find multiple influencers in threshold models of cascading processes with discontinuous phase transitions. This greedy heuristic was designed based on the collective influence theory of threshold models, and applicable to massively large-scale social networks.
  • 10/26/2016 Our paper "Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks" is published in Scientific Reports. Using real information diffusion data from various social platforms, we compared the performance of different methods to identify multiple influencers in social networks.
  • 08/04/2016 I attend the 2016 Joint Statistical Meeting (JSM) in Chicago and give a talk "Improving Influenza Forecast by Counteracting Structural Errors" at the session "Modern Biosurveillance at the Edge of Online Social Media, Social Networks, and Nontraditional Big Data".
  • 05/23/2016 Nine members of Shaman Group attend the MIDAS meeting in Reston, VA. I present my work on error correction in flu forecast at the poster session.