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 Member
USC Norris Comprehensive Cancer Center

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

 

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 Member of USC Norris Comprehensive Cancer Center. 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), the International Congress of Chinese Mathematicians 45-Minute Invited Lecture (2022), NSF Expeditions in Computing (Expeditions) Grant (2022), Centennial Chair in Business Administration (2021, inaugural holder), NSF Focused Research Group (FRG) Grant (2021), 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). She has served as an associate editor of The Annals of Statistics (2022-present), Information and Inference (2022-present), Journal of Business & Economic Statistics (2018-present), Journal of Econometrics (2015-2018), Journal of the American Statistical Association (2014-present), Journal of Multivariate Analysis (2013-2016), and The Econometrics Journal (2012-present).

 
Five representative publications
  • Fan, J., Fan, Y., Han, X. and Lv, J. (2022). SIMPLE: statistical inference on membership profiles in large networks. Journal of the Royal Statistical Society Series B 84, 630-653. [PDF]
  • 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]
  • 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. 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, J. and Fan, Y. (2008). High-dimensional classification using features annealed independence rules. The Annals of Statistics 36, 2605-2637. [PDF]
Some recent talks
  • 2022/04 S. S. Wilks Memorial Seminar in Statistics, Department of Operations Research and Financial Engineering, Princeton University
  • 2022/01 Heidelberg-Mannheim Seminar, Institute for Applied Mathematics, University of Heidelberg
  • 2021/11 Harvard Statistics Colloquium, Department of Statistics, Harvard University
  • 2021/05 Department of Statistics, Stanford University