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 in social networks. Using dynamical and statistical modeling techniques, I work on simulations and forecasts of the seasonal and spatial transmission of various infectious diseases, including influenza, Methicillin-resistant Staphylococcus aureus (MRSA), dengue fever, etc. In parallel, I also use theories in statistical physics and datasets from social platforms to develop and validate practical methods to identify influential spreaders in spreading processes. In a more general scope, I work towards the understanding of the interplay between spreading dynamics and network structure from the perspectives of network science and data science.

Latest News

  • 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.