Tianying Wang

Tianying Wang

Assistant Professor

Research Areas: Quantile regression, measurement error analysis, semi-parametric analysis and high dimensional statistics. The applications of my research include genetic and genomic data analysis (e.g., GWAS, TWAS and sequencing analysis), integrative analysis of genetics and electronic health records data, and epidemiologic research.

Office: Room 212-A, Weiqing Building, Tsinghua University

Phone: +86-10-62787148

Email: tianyingw@tsinghua.edu.cn

Background:

Ph.D., Texas A&M University,  Statistics
Postdoctoral Research Scientist, Columbia University(2018-2020)
Assistant Professor, Center for Statistical Science(2020-now)

Link:https://tianyingw.github.io/

Publications:

  1. Wang, T., Ionita-Laza and Wei, Y. (2021). “Integrated Quantile RAnk Test (iQRAT) for gene-level associations”. The Annals of Applied Statistics. (accepted).
  2. Wang, T., Ling, W., Plantinga, A., Wu, M. and Zhan, X. (2021). “Testing microbiome association using integrated quantile regression models”. Bioinformatics. (accepted).
  3. Wang, T. and Asher, A. (2020). “Improved semiparametric analysis of polygenic gene-environment interactions in case-control studies”. Statistics in Biosciences. (accepted).
  4. Gaynanova, I. and Wang, T. (2019). “Sparse quadratic classification rules via linear dimension reduction”. Journal of Multivariate Analysis, 169, 278-299.
  5. Blas Achic, B. *, Wang, T. * , Su, Y., Kipnis, V., Dodd, K. and Carroll, R. J. (2018). “Categorizing a Continuous Predictor Subject to Measurement Error”. Electronic Journal of Statistics, Vol. 12, No. 2, 4032-4056. ( * joint first authors).
  6. Li, H., Staudenmayer, J., Wang, T., Keadle, S. K., and Carroll, R. J. (2018). “Three‐part joint modeling methods for complex functional data mixed with zero‐and‐one–inflated proportions and zero‐inflated continuous outcomes with skewness”. Statistics in medicine, 37(4), 611-626.
  7. Johnson, V., Payne, R., Wang, T., Asher, A. and Mandal, S. (2017). “On the reproducibility of psychological science”. Journal of the American Statistical Association, 112.517: 1-10.
  8. Wang, T., Yang, Y. and Tian, M. (2017). “Tuning Parameter Selection in Adaptive LASSO for Quantile Regression with Penal Data”. Journal of Applied Statistics and Management, 36(3): 429– 440.
  9. Yin, J., Wang, T., and Wang, W. (2017). “Structure learning and parameter estimation on robust conditional graphical model”. China Science Paper, 12(17): 1921-1929.