Liang Liang's Homepage

Liang Liang

Graduate Student
Department of Statistics
Texas A&M University
3143 TAMU
College Station, TX 77843-3143
Email: liang [at] stat [dot] tamu [dot] edu


  • Ph.D. candidate, Department of Statistics, Texas A&M University, 2012 - Now
       Advisor: Yanyuan Ma and Raymond Carroll.
  • B.S., Department of Mathematics, Tsinghua University, 2008 - 2012

  • Submitted Paper

  • Liang Liang, Raymond Carroll, and Yanyuan Ma, "Dimension Reduction and Estimation in Secondary Analysis of Case-Control Studies"
  • Liang Liang, Yanyuan Ma and Raymond Carroll, "A Semiparametric Efficient Estimator in Case-Control Studies for Gene-Environment Independent Model"
  • Odile Stalder, Alex Asher, Liang Liang, Raymond Carroll, Yanyuan Ma, and Nilanjan Chatterjee, "Semiparametric Analysis of Complex Multi-Marker Gene-Environment Interactions in Case-Control Studies". Biometrika, to apper

  • Teaching

  • STAT 201 Elementary Statistical Inference, Fall 2016

  • Experience and Activities

  • Biostatistics Summer Internship, Takeda Pharmaceuticals, Jun 2016 - Aug 2016
  • Contributed speaker in biomedical applications of nonparametric methods at JSM, Jul 2016
  • Poster presentation at SAMSI workshop, Apr 2016
  • Contributed speaker in semiparametric and nonparametric methods session at ENAR, Mar 2016
  • 2nd Place, Statistical Poster Session Competition, Southeastern Texas Chapter of American Statistical Association, Oct 2015
  • Travel Awards, Southern Regional Council of Statistics, Jun 2015
  • Wining Team, Capital One Student Modeling Competition, Oct 2014 - Nov 2014

  • Graduate Courses

  • Statistical Applications in Probability
  • Advanced Theory of Statistical Inference
  • Measurement Error Model
  • Nonparametric Functional Estimation
  • Time Series Analysis
  • Large Scale Inference
  • Applied Statistics and Data Analysis
  • Advanced Topics in Computational Statistics (R & FORTRAN)
  • Bayesian Model
  • Spatial Statistics
  • Theroy of Linear Model
  • Advance Theory of Statistical Inference
  • Statistical Large Sample Theory
  • Applied Categorical Data Analysis
  • Design of Experiments
  • Machine Learning I: Multivariate Analysis