Yingying Fan  
 
 

Yingying Fan

Centennial Chair in Business Administration
Professor of Data Sciences and Operations
Data Sciences and Operations Department
Marshall School of Business
University of Southern California
Los Angeles, CA 90089

Professor of Economics
University of Southern California

Associate Fellow
USC Dornsife Institute for New Economic
Thinking (INET)

fanyingy (at) marshall.usc.edu
Office: BRI 307B
Phone: (213) 740-9916
Fax: (213) 740-7313

 

Short bio [Picture books by Elizabeth and Charlotte Lu]

Yingying Fan is 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, Professor in Department of Economics at USC, and an Associate Fellow of USC Dornsife Institute for New Economic Thinking (INET). She received her Ph.D. in Operations Research and Financial Engineering from Princeton University in 2007. She was Lecturer in the Department of Statistics at Harvard University from 2007-2008 and Dean's Associate Professor in Business Administration at USC from 2018-2021. Her research interests include statistics, data science, machine learning, economics, big data and business applications, and artificial intelligence and blockchain. Her latest works have focused on statistical inference for networks, texts, and AI models empowered by some most recent developments in random matrix theory and statistical learning theory.

Her papers have been published in journals in statistics, economics, computer science, information theory, and biology. She is the recipient of the Institute of Mathematical Statistics Medallion Lecture (2023), Centennial Chair in Business Administration (2021), NSF Focused Research Group (FRG) Grant (2021), Member of USC Marshall Committee on Promotion & Tenure (P&T Committee, 2021-present), Fellow of Institute of Mathematical Statistics (2020), Associate Member of USC Norris Comprehensive Cancer Center (2020), Fellow of American Statistical Association (2019), Dean's Associate Professor in Business Administration (2018), NIH R01 Grant (2018), the Royal Statistical Society Guy Medal in Bronze (2017), USC Marshall Dean's Award for Research Excellence (2017), the USC Marshall Inaugural Dr. Douglas Basil Award for Junior Business Faculty (2014), the American Statistical Association Noether Young Scholar Award (2013), NSF Faculty Early Career Development (CAREER) Award (2012), Zumberge Individual Award from USC's James H. Zumberge Faculty Research and Innovation Fund (2010), USC Marshall Dean's Award for Research Excellence (2010), and NSF Grant (2009), as well as a Plenary Speaker at the 2011 Institute of Mathematical Statistics Workshop on Finance, Probability, and Statistics held at Columbia University. She has served as an associate editor of Journal of the American Statistical Association (2014-present), Journal of Econometrics (2015-2018), Journal of Business & Economic Statistics (2018-present), The Econometrics Journal (2012-present), and Journal of Multivariate Analysis (2013-2016), as well as on the Institute of Mathematical Statistics Committee for the Peter Hall Early Career Prize (2020-2023) and USC Dornsife Committee on Appointments, Promotions, and Tenure (DCAPT, ad hoc panel member, 2021).

 
Representative Publications
  • Zhu, Z., Fan, Y., Kong, Y., Lv, J. and Sun, F. (2021). DeepLINK: deep learning inference using knockoffs with applications to genomics. Proceedings of the National Academy of Sciences of the United States of America 118, e2104683118. [PDF]
  • Gao, L., Fan, Y., Lv, J. and Shao, Q. (2021). Asymptotic distributions of high-dimensional distance correlation inference. The Annals of Statistics, to appear. [PDF]
  • Fan, J., Fan, Y., Han, X. and Lv, J. (2021). Asymptotic theory of eigenvectors for random matrices with diverging spikes. Journal of the American Statistical Association, to appear. [PDF]
  • Fan, J., Fan, Y., Han, X. and Lv, J. (2021). SIMPLE: statistical inference on membership profiles in large networks. Journal of the Royal Statistical Society Series B, to appear. [PDF]
  • Fan, Y., Demirkaya, E., Li, G. and Lv, J. (2020). RANK: large-scale inference with graphical nonlinear knockoffs. Journal of the American Statistical Association 115, 362-379. [PDF]
  • Fan, Y., Lv, J., Sharifvaghefi, M. and Uematsu, Y. (2020). IPAD: stable interpretable forecasting with knockoffs inference. Journal of the American Statistical Association 115, 1822-1834. [PDF]
  • Fan, Y., Demirkaya, E. and Lv, J. (2019). Nonuniformity of p-values can occur early in diverging dimensions. Journal of Machine Learning Research 20, 1-33. [PDF]
  • Ren, Z., Kang, Y., Fan, Y. and Lv, J. (2019). Tuning-free heterogeneous inference in massive networks. Journal of the American Statistical Association 114, 1908-1925. [PDF]
  • Candès, E. J., Fan, Y., Janson, L. and Lv, J. (2018). Panning for gold: 'model-X' knockoffs for high dimensional controlled variable selection. Journal of the Royal Statistical Society Series B 80, 551-577. [PDF]
  • Fan, Y., Kong, Y., Li, D. and Zheng, Z. (2015). Innovated interaction screening for high-dimensional nonlinear classification. The Annals of Statistics 43, 1243-1272. [PDF]
  • Fan, Y. and Tang, C. (2013). Tuning parameter selection in high dimensional penalized likelihood. Journal of the Royal Statistical Society Series B 75, 531-552. [PDF]
  • Fan, Y. and Li, R. (2012). Variable selection in linear mixed effects models. The Annals of Statistics 40, 2043-2068. [PDF]
  • Fan, J. and Fan, Y. (2008). High-dimensional classification using features annealed independence rules. The Annals of Statistics 36, 2605-2637. [PDF]
  • Fan, J., Fan, Y. and Lv, J. (2008). High dimensional covariance matrix estimation using a factor model. Journal of Econometrics 147, 186-197. [PDF]