Jinchi Lv  
 
 

Jinchi Lv

Kenneth King Stonier 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 Mathematics
University of Southern California

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


jinchilv (at) marshall.usc.edu
Office: BRI 400A
Phone: (213) 740-6603
Fax: (213) 740-7313

 

Short bio

Jinchi Lv is Kenneth King Stonier 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 Mathematics at USC, and an Associate Fellow of USC Dornsife Institute for New Economic Thinking (INET). He received his Ph.D. in Mathematics from Princeton University in 2007. He was McAlister Associate Professor in Business Administration at USC from 2016-2019. His research interests include statistics, machine learning, data science, business applications, and artificial intelligence and blockchain.

His papers have been published in journals in statistics, economics, business, computer science, information theory, neuroscience, and biology, and one of them was published as a Discussion Paper in Journal of the Royal Statistical Society Series B (2008). He is the recipient of the International Congress of Chinese Mathematicians 45-Minutes Invited Lecture (2022), NSF Emerging Frontiers (EF) Grant (2022), Fellow of American Statistical Association (2020), NSF Grant (2020), Kenneth King Stonier Chair in Business Administration (2019), Fellow of Institute of Mathematical Statistics (2019), Member of USC University Committee on Appointments, Promotions, and Tenure (UCAPT, 2019-2021), USC Marshall Dean's Award for Research Impact (2017), Adobe Data Science Research Award (2017), McAlister Associate Professor in Business Administration (2016), Simons Foundation Grant (2016), the Royal Statistical Society Guy Medal in Bronze (2015), NSF Faculty Early Career Development (CAREER) Award (2010), USC Marshall Dean's Award for Research Excellence (2009), NSF Grant (2008), and Zumberge Individual Award from USC's James H. Zumberge Faculty Research and Innovation Fund (2008). He has served as an associate editor of the Annals of Statistics (2013-2018), Journal of Business & Economic Statistics (2018-present), and Statistica Sinica (2008-2016).



Representative Publications

  • Zheng, Z., Lv, J. and Lin, W. (2021). Nonsparse learning with latent variables. Operations Research 69, 346-359. [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]
  • Gao, L., Fan, Y., Lv, J. and Shao, Q. (2021). Asymptotic distributions of high-dimensional distance correlation inference. The Annals of Statistics 49, 1999-2020. [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. and Lv, J. (2019). Nonuniformity of p-values can occur early in diverging dimensions. Journal of Machine Learning Research 20, 1-33. [PDF]
  • Uematsu, Y., Fan, Y., Chen, K., Lv, J. and Lin, W. (2019). SOFAR: large-scale association network learning. IEEE Transactions on Information Theory 65, 4924-4939. [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]
  • Lu, Y., Fan, Y., Lv, J. and Noble, W. S. (2018). DeepPINK: reproducible feature selection in deep neural networks. Advances in Neural Information Processing Systems (NeurIPS 2018). [PDF]
  • Fan, Y. and Lv, J. (2016). Innovated scalable efficient estimation in ultra-large Gaussian graphical models. The Annals of Statistics 44, 2098-2126. [PDF]
  • Lv, J. and Liu, J. S. (2014). Model selection principles in misspecified models. Journal of the Royal Statistical Society Series B 76, 141-167. [PDF]
  • Fan, Y. and Lv, J. (2013). Asymptotic equivalence of regularization methods in thresholded parameter space. Journal of the American Statistical Association 108, 1044-1061. [PDF]
  • Lv, J. (2013). Impacts of high dimensionality in finite samples. The Annals of Statistics 41, 2236-2262. [PDF]
  • Fan, J. and Lv, J. (2008). Sure independence screening for ultrahigh dimensional feature space (with discussion). Journal of the Royal Statistical Society Series B 70, 849-911. [PDF]