Ying Zhu

Ying Zhu is an Associate Professor in the Department of Economics at the University of California, San Diego. She holds a Ph.D. from the University of California, Berkeley, awarded in 2015. Her research focuses on high-dimensional estimation and inference, nonasymptotic statistics, semiparametric and nonparametric methods, panel data analysis, and the integration of economic models with machine learning and operations research. Zhu is affiliated with the Econometrics research group. Her work explores the intersection of statistical theory and economic applications, emphasizing methodological innovation and practical relevance.

Ying Zhu is a researcher at UCSD with expertise in Econometrics and Statistics. Her work focuses on high-dimensional statistical methods, particularly in econometric models with endogenous regressors and instruments. She explores the theoretical properties of machine learning techniques in econometric applications, including sparse linear models, semiparametric regression, and nonasymptotic analysis. Her research also addresses issues such as omitted variable bias, permutation invariant functions, and the limitations of data-based price discrimination. Zhu's contributions span both theoretical and applied econometrics, with an emphasis on statistical inference and computational efficiency in complex models.