Xin Lu

Xin Lu

Affiliated to research

About me

Xin is a computer scientist with extensive experience in quantitative sociology and public health. His research includes analytics for big data, social networks, and statistical sampling techniques. In addition to the study of human behaviors during disasters and aid emergency responses with mobile phone data, he is developing disease outbreak models for syndromic surveillance system as well as implementing sampling techniques and big data analytics for high risk population that are HIV-related. Dr. Lu is awarded China's “The National Science Fund for Distinguished Young Scholars” in 2020.

Research description

Dr. Xin's research is multidisciplinarily distributed in the following areas:

  • Disaster response with big data analytics, e.g., mobile phone data, satellite imagery, IoT, online social networking data, etc.
  • Evaluating and improving network sampling strategies (e.g., respondent-driven sampling, RDS) and make statistical inference for hard-to-access populations
  • Network-based epidemic modeling of infectious diseases
  • Operational research, graph algorithms
  • Anomaly detection models for symdromic surveillance systems
  • Other issues in big data, mobility, complex networks, social networks, complex systems, organizational dynamics, human behaviors, etc.

Selected Publications

  • Tan S, et al., Mobility in China, 2020: a tale of four phases. National Science Review, 2021. 8(11): p. nwab148. (pdf)
  • Lu X, et al., Mobile Phone-Based Population Flow Data for the COVID-19 Outbreak in Mainland China. Health Data Science, 2021. p. 9796431. (pdf)
  • Jia J, Li Y, Lu X, et al., Triadic embeddedness structure in family networks predicts mobile communication response to a sudden natural disaster. Nature Communications, 2021. 12(1): p. 4286. (pdf)
  • Jia J, Lu X, et al., Population flow drives spatio-temporal distribution of COVID-19 in China. Nature. 2020. 582(7812): 389-394. (pdf)
  • Zhou B, Lu X, and Holme P, Universal evolution patterns of degree assortativity in social networks. Social Networks. 2020. 63: p. 47-55. (pdf)
  • Kraemer M, et al., Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus. Nature Microbiology, 2019. 3: p. 1-10. (pdf)
  • Lu X, Horn AL, Su J & Jiang J, A Universal Measure for Network Traceability. Omega, 2019. doi: https://doi.org-/10.1016/j.omega.2018.09.004. (pdf)
  • Chen S, Lu X, Liu Z & Jia Z, Sampling on bipartite networks: a comparative analysis of eight crawling methods. Journal of Statistical Mechanics: Theory and Experiment, 2018. 2018(7): p. 073403. (pdf)
  • Zhang, Z-K, et al., Dynamics of information diffusion and its applications on complex networks. Physics Reports, 2016. 651: p. 1-34. (pdf)
  • Lu X, et al., Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen. Climatic Change, 2016. 138(3): p. 505-519. (pdf)
  • Lu X, et al., Unveiling hidden migration and mobility patterns in climate stressed regions: A longitudinal study of six million anonymous mobile phone users in Bangladesh. Global Environmental Change, 2016,38:1-7. (pdf)
  • Cheng Q, Lu X, Zhong L, Huang J, Mining research trends with anomaly detection models: the case of social computing research. Scientometrics, 2015. 103(2): p. 453-469. (pdf)
  • Lu X and Brelsford C, Network Structure and Community Evolution on Twitter: Human Behavior Change in Response to the 2011 Japanese Earthquake and Tsunami. Scientific Reports, 2014. 4. ( pdf)
  • Buckee CO, Tatem AJ, Wetter E, Lu X & Bengtsson L, Society: Protect privacy of mobile data. Nature, 2014. 514(7520): p. 35-35. (correspondence, pdf)
  • Lu X, et al., Approaching the limit of predictability in human mobility. Scientific Reports, 2013. 3. (pdf)
  • Lu X, Linked Ego Networks: Improving estimate reliability and validity with respondent-driven sampling. Social Networks, 2013. 35(4): p. 669-685. (pdf)
  • Lu X, Bengtsson L, & Holme P, Predictability of population displacement after the 2010 Haiti earthquake. Proceedings of the National Academy of Sciences, 2012. 109(29): p. 11576-11581. (pdf)
  • Lu X, et al., The sensitivity of respondent-driven sampling. Journal of the Royal Statistical Society: Series A (Statistics in Society), 2012. 175(1): p. 191-216. (pdf)
  • Bengtsson L, Lu X, Thorson A, Garfield R, Schreeb J, Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti. PLoS Medicine, 2011. 8(8): p. e1001083. (pdf)