Ph.D., Applied Mathematics, Université Paris-Saclay (former Université Paris-Sud Orsay), 1990
Master (D.E.A., Statistics), Université Paris-Saclay (former Université Paris-Sud Orsay), 1986

Professor Yao worked as an Assistant/Associate Professor in the University of Paris I Panthéon-Sorbonne from 1990 to 2000. From 2000, he has been a Full Professor of applied mathematics in the University of Rennes 1. He also held visiting positions at SUDIMAGE R&D from 1989-1994, and at INRIA, France from 2003-2004. From 2011, he worked at the University of Hong Kong as an Associate/Full Professor. He currently also serves as a Special Guest Professor at the School of Mathematics, Shandong University.

Professor Yao is an international leading scholar in random matrix theory and high-dimensional statistics. His book Large Sample Covariance Matrices and High-Dimensional Data Analysis (Cambridge University Press, 2015, co-authored with Zhidong Bai and Shurong Zheng) is an authoritative reference of the field. Professor Yao received a few awards for his research contributions. He was elected as a Fellow of the Institute of Mathematical Statistics (U.S.A.) with the citation: “For influential contribution to the inferential aspects of random matrix theory in the analysis of high-dimensional data”. He is also an Elected Member of the International Statistical Institute. Professor Yao is currently a  scientific secretary and a member of the Executive Committee of the Bernoulli Society for Mathematical Statistics and Probability. He has been on the editorial board of several leading journals including Journal of Multivariate Analysis, Random Matrices: Theory and Applications and Bernoulli.

[A] Book

Jianfeng Yao, Shurong Zheng and Zhidong Bai. Large Sample Covariance Matrices and High-Dimensional Data Analysis (Cambridge University Press, 2015, co-authored with Zhidong Bai and Shurong Zheng)

[B] Journal papers

1. Zeng Li, Fang Han and Jianfeng Yao, 2020. Asymptotic joint distribution of extreme eigenvalues and trace of    large sample covariance matrix in a generalized spiked population model. The Annals of Statistics 48(6) (December), 3138-3160
2. Jian Song, Jianfeng Yao and Wangjun Yuan, 2020. High-dimensional limits of eigenvalue distributions for general Wishart process. The Annals of Applied Probability 30 (4) (August), 1642-1668
3. Z. Li, C. Lam, J. Yao and Q. Yao, 2019. On testing for high-dimensional white noise. The Annals of Statistics, 47(6) (December 2019), 3382-3412
4. Weiming Li and Jianfeng Yao 2018. On structure testing for component covariance matrices of a high-dimensional mixture. Journal of the Royal Statistical Society Series B (Statistical Methodology) 80 (Part 2) (February), 293-318
5. Q. Wang and J. Yao, 2017. Extreme eigenvalues of large-dimensional spiked Fisher matrices with application. The Annals of Statistics 45(1) (February), 415-460.
6. Z. Li, Q. Wang and J. Yao, 2017. Identifying the number of factors from singular values of a large sample auto-covariance matrix. The Annals of Statistics 02/2017; 45(1) (February), 257-288
7. Qinwen Wang and Jianfeng Yao, 2016. Moment approach for singular values distribution of a large auto-covariance matrix. Annals de l’Institut Henri Poincaré – Probabilités et Statistiques 52 (4), 1641-1666
8. D. Passemier, Zh. Li and J. Yao, 2017. On estimation of the noise variance in high-dimensional probabilistic principal component analysis. Journal of the Royal Statistical Society Series B (Statistical Methodology) 79(1) (January), 51-67.
9. S. Zheng, Z. D. Bai and J. Yao, 2017. CLT for eigenvalue statistics of high-dimensional general Fisher matrices with applications. Bernoulli 23(2) (April), 1130-1178.
10. S. Zheng, Z. D. Bai and J. Yao, 2015. Substitution principle for CLT of linear spectral statistics of high-dimensional sample covariance matrices with applications to hypothesis testing. The Annals of Statistics 43 (2), 546-591
11. Q. Wang, Z. Su and J. Yao, 2014. Joint CLT for several random sesquilinear forms with applications to large-dimensional spiked population models. Electron. J. Probab. 19 (103), 1-28.
12. C. Wang, H. Liu, J.F. Yao, R. Davis and W. K. Li, 2014. Self-excited Threshold Poisson Autoregression.  J. Amer. Statist. Assoc. 109 (506, June 2014), 777-787
13. N. Raillard, P. Ailliot and J. Yao, 2014. Modeling extreme values of processes observed at irregular time steps: application to significant wave height. The Annals of Applied Statistics 8 (1) (March), 622–647
14. T. Crivelli, B. Cernuschi-Frias, J.F. Yao and P. Bouthemy, 2013. Motion textures: modeling, classification and segmentation using mixed-state Markov random fields. SIAM J. Imaging Science 6(4), 2484–2520.
15. T. Crivelli, P. Bouthemy, B. Cernuschi-Frı́as and J. Yao, 2011. Simultaneous motion detection and background reconstruction with a conditional mixed-state Markov random field. Int. J. Computer Vision 94, 295–316.
16. Z. D. Bai, D. Jiang, J. Yao and S. Zheng, 2009. Corrections to LRT on Large Dimensional Covariance Matrix by RMT. Ann. Statistics 37 (6B), 3822-3840.
17. Z. D. Bai and J.-F. Yao, 2008. Central limit theorems for eigenvalues in a spiked population model. Annals de l’Institut Henri Poincaré – Probabilités et Statistiques 44 (3), 447-474.
18. C. Hardouin and J.-F. Yao, 2008. Multi-parameter auto-models and their applications. Biometrika 95, 335-349.
19. P. Bouthemy, C. Hardouin, G. Piriou and J.-F. Yao, 2006. Mixed-state auto-models and motion texture modeling. Journal of Mathematical Imaging and Vision, 25, 387-402.
20. Z. D. Bai and J.-F. Yao, 2005. On the convergence of the spectral empirical process of Wigner matrices. Bernoulli, 11(6):1059-1092.
21. B. De Saporta et J.-F. Yao, 2005. Tail of a linear diffusion with Markov switching. Annals of Applied Probability, 15(1B):992-1018.
22. C. Gaetan and J.-F. Yao, 2003. A multiple imputation Metropolis version of the EM algorithm. Biometrika, 90(3):643-654.
23. Z. D. Bai, B.-Q. Miao and J.-F Yao, 2002. Convergence rates of spectral distributions of large sample covariance matrices. SIAM J. Matrix Analysis 25(1):105-127.
24. J.F. Yao and J.G. Attali, 2000. On stability of nonlinear AR processes with Markov switching. Adv. Applied Probab., 32:394-407.

[C] Top conferences in Computer Science

1. T. Crivelli, B. Cernuschi-Frias, P. Bouthemy, J.F. Yao, 2009. Learning mixed-state Markov models for statistical motion texture tracking In Inter. Conf. Computer Vision (ICCV’09), Kyoto, Japan, September 2009.
2. T. Crivelli, G. Piriou, B. Cernuschi-Frias, P. Bouthemy, J.F. Yao, 2008. Simultaneous motion detection and background reconstruction with a mixed-state conditional Markov random field. In Proc. Eur. Conf. Computer Vision (ECCV’08), Volume 1, Pages 113-126, Marseille, France
3. G. Piriou, P. Bouthemy, J-F. Yao, 2004. Extraction of semantic dynamic content from videos with probabilistic motion models. In European conference on computer vision, ECCV’04, Prague

For full list of publication, see my Google Scholar page.