معرفی
Lan Gao is an Assistant Professor in the Department of Business Analytics and Statistics at the University of Tennessee's Haslam College of Business. She holds a PhD in Statistics from the Chinese University of Hong Kong (2019) and completed postdoctoral training at the University of Southern California. Her research develops statistical methods for high-dimensional data, nonparametric inference, and privacy-preserving machine learning.
Research expertise spans: high-dimensional statistical inference, asymptotic theory, knockoff frameworks for variable selection, dynamic pricing models, and distributional nearest-neighbor methods. Applications include econometrics, privacy-constrained analytics, and large-scale inference.
Publications address foundational questions in modern statistical learning, including FDR control in knockoff methods, privacy-compliant inference, and optimal nonparametric estimation techniques. Theoretical work connects with applications in pricing optimization and dependence testing.
Awards: IMS New Researcher Travel Award (2021) for contributions to statistical theory.
