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Lily Wang is a professor of Statistics at George Mason Â鶹¹ú²ú. She received her PhD in Statistics from Michigan State Â鶹¹ú²ú in 2007. Prior to joining Mason in 2021, she was on the faculty of Iowa State Â鶹¹ú²ú (2014-2021) and the Â鶹¹ú²ú of Georgia (2007-2014).

Wang is highly regarded internationally for her work on non/semi-parametric regression methods. She has broad interests in statistical learning of data objects with complex features, methodologies for functional data, spatiotemporal data, imaging data, and survey sampling. Working at the interface of statistics, mathematics, and computer science, she is also interested in developing cutting-edge statistical methods for solving issues related to data science and big data analytics. The methods she developed have a wide application in economics, engineering, neuroimaging, epidemiology, environmental studies, and biomedical science.

She is a fellow of both the Institute of Mathematical Statistics (2020) and the American Statistical Association (2021) and an Elected Member of the International Statistical Institute (2008). She is the recipient of multiple NSF awards, SEC Research Fellowship (2019-2020) and ASA/NSF/BLS Senior Research Fellowship (2020-2011), the Mid-Career Achievement in Research Award (2021), the COVID-19 Exceptional Effort Research Impact Award (2021) from Iowa State Â鶹¹ú²ú and the M. G. Michael Research Award (2012) from the Â鶹¹ú²ú of Georgia.

Wang serves on the editorial boards of the Journal of the Royal Statistical Society, Series B, Journal of Nonparametric Statistics, and Statistical Analysis and Data Mining.