Lily Wang

Lily Wang is a Professor of Statistics at George Mason University. She earned her Ph.D. in Statistics from Michigan State University in 2007. Prior to joining Mason in 2021, she was on the faculty of Iowa State University (2014-2021) and the University of Georgia (2007-2014). Her primary areas of research include non/semi-parametric modeling and inference, statistical learning of data objects with complex features, methodologies for functional data, spatiotemporal data, imaging, and data reduction methods. Working at the interface of statistics, mathematics, and computer science, she is also interested in developing methodologies and theories that advance the field of data science and big data analytics. The methods she developed have a wide application in engineering, neuroimaging, epidemiology, environmental studies, economics, and biomedical science. 

Dr. Wang is a fellow of both the Institute of Mathematical Statistics and the American Statistical Association and an Elected Member of the International Statistical Institute. She is currently serving on the editorial board of Journal of the Royal Statistical Society, Series B (2020-), Statistica Sinica (2023-), Journal of Nonparametric Statistics (2019-), and Statistical Analysis and Data Mining (2018-).

Dr. Wang has served as a Financial Economist at the U.S. Securities and Exchange Commission and as a Senior Research Fellow at the U.S. Bureau of Labor Statistics, where she contributed her expertise in the development and evaluation of advanced statistical methodologies and data science techniques.