Yingying Fan is a Chinese-American statistician and Centennial Chair in Business Administration and Professor in Data Sciences and Operations Department of the Marshall School of Business at the University of Southern California. [1] She is currently the Associate Dean for the PhD Program at USC Marshall. She also holds joint appointments at the USC Dana and David Dornsife College of Letters, Arts and Sciences, and Keck Medicine of USC. Her contributions to statistics and data science were recognized by the Royal Statistical Society Guy Medal in Bronze in 2017 [2] and the Institute of Mathematical Statistics Medallion Lecture in 2023. [3] She was elected Fellow of American Statistical Association in 2019 [4] and Fellow of Institute of Mathematical Statistics for seminal contributions to high-dimensional inference, variable selection, classification, networks, and nonparametric methodology, particularly in the field of financial econometrics, and for conscientious professional service in 2020. [5]
Fan, along with her collaborators, has developed some popular statistical and data science tools including the generalized information criterion (GIC), the model-X knockoffs (MXK), the deep learning inference using knockoffs (DeepLINK), and the statistical inference on membership profiles in large networks (SIMPLE), as well as some fundamental asymptotic theory for high-dimensional random forests and the eigenvectors of large random matrices.
Some of her representative publications include:
She currently co-edits the Journal of Business & Economic Statistics. In addition, she serves as the IMS editor of Statistics Surveys as well as the IMS-CUP coordinating editor for the IMS-Cambridge University Press Textbooks/Monographs Series.
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Jinchi Lv is a statistician, data scientist, professor of mathematics, and a chaired professor in the Data Sciences and Operations department of the Marshall School of Business at the University of Southern California. He is a co-developer of several data science learning, inference methods and algorithms for feature screening, model selection with misspecification, large Gaussian graphical models, and feature selection with controlled error rates such as the sure independence screening (SIS), the generalized Bayesian information criterion with prior probability (GBICp), the innovated scalable efficient estimation (ISEE), and the model-X knockoffs (MXK). He is an elected fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the recipient of the Royal Statistical Society's Guy Bronze Medal.