Research Areas

My research program focuses on the development of statistical
models and methods that are motivated by data analysis challenges
from real-world applications.

• Causal inference and machine learning
  
  1. Treatment effect estimation, treatment rules
  2. Out-of-distribution generalization
  3. Invariant models and methods
  4. Feature representation learning
  
  
• Network or graphical models for omics data
  
  
• Unsupervised learning of genomics and proteomics data
  
  
• Robust methods for stochastic gradient descent (SGD)
  
  

  Grant support

  
  
  
  
  

  Past Publications

  1. Cauchy combination test, published in JASA (Journal of the American Statistical Association)
     A test for weak and sparse alternatives with analytic p-value calculation and under arbitrary dependency structures
  2. Another paper in JASA
     "Accurate and Efficient P-value Calculation via Gaussian Approximation: a Novel Monte-Carlo Method"
  3. In the Annals of Applied Statistics 2018, Vol. 12, No. 1, 567-585.
     "Powerful test based on conditional effects for genome-wide screening"

  Google Scholar Citation

  Curriculum Vitae
  
  

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  Education

  1994: B.S. in Statistics

  Department of Probability and Statistics, Peking University

  1997: M.S. in Statistics

  Department of Probability and Statistics, Peking University

  2000: Ph.D. in Statistics

  Department of Statistics, University of California at Los Angeles

  
  

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  Research Group

  • Pengcheng Yang
  • Muye Liu
  • Haoze Li
  • Jinyong Lee
  
  

  Past group members in recent years

  • Donglai Chen, PhD 2019
  • Yaowu Liu, PhD 2017
  • Won Chul Song, Postdoc 2015-2017
  • Zhongyuan Chen, 2020-2022
  
  

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