GuanNan Wang

GuanNan Wang is an Associate Professor in the Department of Mathematics at the College of William & Mary, where he teaches courses in probability, mathematical statistics, and data science. He holds a Ph.D. in Statistics from the University of Georgia (2015), and also earned two M.Sc. degrees in Computer Science and Statistics from the same institution.

• Ph.D., Statistics, University of Georgia, 2015
• M.Sc., Computer Science, University of Georgia, 2015
• M.Sc., Statistics, University of Georgia, 2010

His research focuses on statistical learning, large-scale data analysis, non- and semi-parametric statistics, spatial data analysis, and functional data modeling. He develops advanced statistical methodologies with applications in public health, environmental science, and data science. His work often involves spline-based estimation, varying coefficient models, and spatiotemporal modeling.

The recent publications highlight a strong trend toward applied statistical modeling in epidemiology, particularly in the context of the COVID-19 pandemic. His research integrates anomaly detection, data fusion, and real-time forecasting, as demonstrated by his contributions to the COVID-19 Dashboard. The publications collectively emphasize nonparametric and semiparametric methods, spatial statistics, and robust inference.

His scientific contributions include work published in top-tier journals such as Journal of the American Statistical Association, Biometrics, Statistica Sinica, and Journal of Nonparametric Statistics.

Wang actively contributes to data-driven public health initiatives, advising on statistical modeling and leading efforts in infectious disease forecasting. He has not received any explicitly mentioned awards or fellowships. He leads a research group focused on spatiotemporal modeling and has developed tools for real-time risk analysis of infectious diseases. His lab is involved in the integration and analysis of multi-source public health data.