罗珊Shan Luo
长聘教轨副教授Tenure-track Associate Professor

办公室??Office：
6 号楼 527
办公接待时间??Office Hour：
Friday 2:00-5:00pm
办公室电话??Office Phone：
**
E-mail：
sluomath at sjtu.edu.cn
教育背景??Education：
博士，2012，新加坡国立大学
Ph.D., 2012, National University of Singapore

研究兴趣??Research Interests：
高维数据中的模型选择标准、变量选择方法和统计推断
model selection criteria, variable selection methods and statistical inference in high-dimensional data

教育背景/经历　Education
2001-2005, 北京航空航天大学，获学士学位
2005-2008，北京大学，获硕士学位
2008-2012，新加坡国立大学，获博士学位
工作经历　Work Experience
2014.7-至今：上海交通大学长聘教轨副教授
2018.8-2019.7：密歇根大学生物统计系访问****
科研
In Preparation:
1.Matrix variate regression, with Z.Xu and Z.Chen.
Manuscripts:
1. Forward selection in linear regression models based on robust estimation, with Z.Chen.
Submitted:
1.FDR control in large scale tests of pairwise independence: a first step.
2.Hypothesis testing for high dimensional regression models, with W. Liu.
3. A Portmanteau Local Feature Discrimination Approach to the Classification with High-dimensional Matrix-variate Data, with Z.Xu andZ.Chen.
Publications:
9:S.Luoand Z. Chen. (2021). Sequential interaction group selection by the principle of correlation search for high-dimensional interaction models. Statistica Sinica, 31(1), 197--221.
8: S.Luo and Z. Chen. (2020). A procedure of linear discrimination analysis with detected sparsity structure for high-dimensional multi-class classification. Journal of Multivariate Analysis, (179) (2020) 104641.
7: S.Luo and Z. Chen. (2020). Feature selection by canonical correlation search in high-dimensional multi-response models with complex group structures. Journal of the American Statistical Association, (115) (2020) 1227--1235.
6.S.Luo.(2020).Variable selection in high-dimensional sparse multiresponse linear regression models. Statistical Papers. (61):1245-1267.
5.S.Luo, J.Xu and Z. Chen. (2015). Extended Bayesian information criterion in the Cox model with a high-dimensional feature space. Annals of the Institute of Statistical Mathematics, (67) (2015) 287--311.
4.S.Luo and Z. Chen. (2014a). Sequential Lasso cum EBIC for feature selection with ultra-high dimensional feature space. Journal of the American Statistical Association,(109) (2014) 1229--1240 .
3.S.Luo and Z. Chen. (2014b). Edge detection in sparse Gaussian graphical models. Computational Statistics and Data Analysis, (70) (2014) 138--152.
2.S.Luo and Z. Chen. (2013a). Extended BIC for linear regression models with diverging number of relevant features and high or ultra-high feature spaces. Journal of Statistical Planning and Inference (143) (2013) 494--504.
1.S.Luo and Z. Chen. (2013b). Selection consistency of EBIC for GLIM with non-canonical links and diverging number of parameters. Statistics and Its Interface, (6) (2013) 275--284.

教学
1. AY2013-2014,semester 1; AY2014-2015,semester 1
Stochastic processes (English)
2. AY2013-2014,semester 2
Bayesian statistics (Chinese)
3:AY2014-2015,semester 2
Statistical inference and decision theory (Chinese)
4:AY2015-2016,semester 1; AY2016-2017,semester 1; AY2017-2018, semester 1
(Applied) Multivariate Statistical Analysis (Chinese)
5:AY2016-2017,semester 2;AY2017-2018, semester 2
Probability and Statistics (Chinese)
6: AY2017-2018, semester 1
Basic Mathematical Statistics (English)




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