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, and Professor in Department of Mathematics at USC. 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. He is the recipient of the International Congress of Chinese Mathematicians 45-Minute Invited Lecture (2023), 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-present), 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), Journal of the Royal Statistical Society Series B Discussion Paper (2008), 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 Journal of the American Statistical Association (2023-present), Journal of Business & Economic Statistics (2018-present), The Annals of Statistics (2013-2018), and Statistica Sinica (2008-2016).
Five representative publications
Some recent talks
- 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]
- 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]
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. (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]
- 2022/05 Econometrics Seminar, Faculty of Economics, University of Cambridge
- 2022/03 S. S. Wilks Memorial Seminar in Statistics, Department of Operations Research and Financial Engineering, Princeton University
- 2022/03 Department of Biostatistics, School of Global Public Health, New York University