Summary

Research Interests

Latent variable modeling, Bayesian spatial and spatio-temporal analysis, Bayesian parametric and nonparametric regression models, statistical computing, advanced Markov chain Monte Carlo methods, infectious disease modeling/forecasting, Group testing data analysis, pooled biomarker modeling, and biostatistical and epidemiological applications.

Publications

• w88, Y., Watson, S., Lund, R., Nordone, S., McMahan, C., and Yabsley, M. (2017). A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States., 12(7): e0182028..
• Watson, S., w88, Y., Gettings, J., Lund, R., Nordone, S., McMahan, C., and Yabsley, M. (2017). A Bayesian Spatio-Temporal Model for Forecasting the Prevalence of Antibodies toBorrelia burgdorferi,Causative Agent of Lyme Disease, in Domestic Dogs within the Contiguous United States.PLOS ONE,12(5): e0174428.
• w88, Y., McMahan, C., and Gallagher, C. (2017+). A general regression framework for the regression analysis of pooled biomarker assessments.Statistics in Medicine, 36(15), 2363-2377.
• w88, Y., Lund, R., Nordone, S., Yabsley, M., and McMahan, C. (2017). A Bayesian spatio-temporal model for forecasting the prevalence of antibodies toEhrlichiain domestic dogs within the contiguous United States.Parasites Vectors, 10:138.
• Bowman, D., w88, Y., McMahan, C., Nordone, S., Yabsley, M., and Lund, R. (2016). Forecasting United States heartwormdiroflaria immitisprevalence in dogs.Parasites Vectors, 9:540.
• Yao, C., w88, Y., and Zhan, M. (2011). Frequency-resonance-enhanced vibrational resonance in bistable systems, it Physical Review E, bf 83, 061122.
• Zhao, Q., Yi, M. and w88, Y. (2011). Spatial distribution and dose-response relationship for different operation modes in a reaction-diffusion model of the MAPK cascade, Physical Biology, bf 8, 055004.

Education

• PhD from Clemson University, Clemson, SC