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  • Resume
  • Research Fields
  • Enrollment and Training
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Xingjie Shi

  • About

    • Department: Academy of Statistics and Interdisciplinary Sciences
    • Gender: male
    • Post:
    • Graduate School: Shanghai University of Finance and Economics
    • Degree: PhD
    • Academic Credentials:
    • Tel:
    • Email: xjshi@fem.ecnu.edu.cn
    • Office: A1516, Science Building
    • Address: 3663 North Zhongshan Rd
    • PostCode: 200062
    • Fax:

    WorkExperience

    • Jul 2021, East China Normal University, FEM, Associate Professor

    • Sep 2012 - May 2014, Yale University, Department of Biostatistics, Graduate Fellow

    Education

    Sep 2009 - Jun 2014, Shanghai University of Finance and Economics, PhD in Statisitcs


    Resume

    Homepage: https://xingjieshi.github.io/

    Other Appointments

    • Elected Member, International Statistical Institute, 2018 - Present

    • Member, International Mathematical Statistics, 2018 - Present

    • Member, International Chinese Statistical Association, 2014 - Present

    • Member, China Association for Applied Statistics, 2017 - Present.

    Research Fields

    My research interests lie at the intersection of statistics, computation and modeling with a focus on large scale genetic/genomic data.


    Enrollment and Training

    Course

    Scientific

    PI, National Natural Science Foundation of China (71501089) "Variable Selection for the Monotone

    Transformation Models and Its Applications in Loan Defaults", 2016-2018


    Academic Achievements

    1.     X. Shi,X. Chai, Y. Yang, Q. Cheng, Y Jiao, H Chen, J Huang, C. Yang, and J. Liu (2020) A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies. Nucleic Acids Research. [SCI, impact factor: 17.0], 48(19): e109

    2.     Q. Cheng, Y. Yang, X. Shi, K. Yeung, C. Yang, H. Peng, J. Liu (2020) MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting for linkage disequilibrium and horizontal pleiotropy. NAR Genomics and Bioinformatics, 2(2): lqaa028

    3.     X. Shi, S. Ma and Y. Huang (2020). Promoting Sign Consistency in the Cox Proportional Hazards Cure Model Selection. Statistical Method in Medical Research. 29(1):15-28 [SCI]

    4.     X. Shi, S. Wang, and Y. Li (2020) Robust Binary Classification of High-dimensional Data. 37(9), Statistical Research, 95-105 [Chinese]

    5.     Y. Yang, X. Shi, Y. Jiao, J. Huang, M. Chen, X. Zhou, L Sun, X. Lin, C. Yang and J. Liu (2020) CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies. Bioinformatics, 36(7): 2009-16 [SCI]

    6.     X. Shi, Y. Yang, Y. Jiao, C. Cheng, C. Yang, X. Lin and J. Liu (2019). VIMCO: variational inference for multiple correlated outcomes in genome-wide association studies. Bioinformatics, 35(19), 3693-3700. [SCI]

    7.     S. Wang, X. Shi, M. Wu, and S. Ma (2019) Horizontal and vertical integrative analysis methods for mental disorders omics data. Scientific Report, 9(1):13430 [SCI]

    8.     Y. Sun, X. Shi, M. Wu and S. Ma (2019) Community Detection in Genetic Network Big Data: Taking NC Method as an Example 36(3), Statistical Research, 124-128 [Chinese]

    9.     X.Shi, Y. Huang, J. Huang and S. Ma (2018). A forward and backward stagewise algorithm for nonconvex loss functions and adaptive lasso. Computational Statistics and Data Analysis, 124, 235-251.

    10.   H.Chai#, X. Shi#, Q. Zhang, Q. Zhao, Y. Huang and S. Ma (2017) Analysis of cancer geneexpression data with an assisted robust marker identification approach. Genetic Epidemiology, 779-789.

    11.   Y. Jiang#, X. Shi#, Q. Zhao, M. Krauthammer, BE Rothberg & S. Ma (2016) Integrated analysis of      multidimensional omics data on cutaneous melanoma prognosis. Genomics, 107(6):223-30.

    12.   J. Liu, C. Yang, X. Shi, C. Li, J. Huang, H. Zhao & S. Ma. (2016) Analyzing Association Mapping in Pedigree-Based GWAS Using a Penalized Multitrait Mixed Model. Genetic Epidemiology, 40(5):382-393.

    13.   X. Shi, Q. Zhao, J. Huang, Y. Xie & S. Ma (2015) Deciphering the association between geneexpression and copy number alteration using a sparse double Laplacian shrinkage approach. Bioinformatics, 31(24): 3977-3983.

    14.   Q. Zhao, X. Shi, J. Huang, J. Liu, Y. Li, S. (2015). Integrative analysis of -omics data using penalty functions. WIREs Computational Statistics, 7(1): 99-108.

    15.   X. Shi#, H. Yi#, & S. Ma (2015) Measures for the degree of overlap of gene signatures and applications to TCGA. Briefings in bioinformatics, 16(5): 735-744.

    16.   C.Wu, X. Shi, Y.Cui, S. Ma (2015) A penalized robust semiparametric approach for gene environment interactions. Statistics in Medicine, 34 (30): 40164030.

    17.   Q. Zhao# , X. Shi#, Y. Xie, J. Huang, B. Shia and S. Ma (2015) Combining Multidimensional GenomicMeasurements for Predicting Cancer Prognosis: Observations from TCGA. Briefing in Bioinformatics, 16(2): 291-303.

    18.   X. Shi, J. Liu, J. Huang, Y. Zhou, B. Shia and S. Ma (2014) Integrative Analysis of Cancer PrognosisData with Contrasted Group Bridge Penalization.Genetic Epidemiology, 38(2): 141-151.

    19.   X. Shi, J. Liu, J. Huang, Y. Zhou, Y. Xie and S. Ma (2014) A Penalized Robust Method for identifyingGene-Environment Interactions. Genetic Epidemiology, 38(3): 220-230.

    20.   X. Shi, S. Shen, J. Liu, J. Huang, Y. Zhou and S. Ma (2013) Similarity of Markers Identified from Cancer Gene Expression Studies: Observations from GEO. Briefing in Bioinformatics. 15(5): 671-684.



    Honor