Research Interests

• Bayesian modeling, theory, and computation; Even Bill Gates talks about Bayesian ideas!
• Bayesian model selections, variable selections, and neuronized priors.
• Statistical learning methods: factor models, index models, variation methods, Gaussian process regression, etc.
• High-dimensional methods: FDR control methods for linear and nonlinear methods, sliced inverse regression.
• Nonparametric modeling and testing: two-sample test, interaction test, generative bootstrap sampler.
• Statistical missing data problems, imputation methodology, causal inference.
• Gibbs sampling and other MCMC methods. See my book (sample chapters: introduction, Metropolis, and HMC)on the topics.
• Monte Carlo filters, Sequential Monte Carlo; see an overview of Liu & Chen (1998), and its broad applications in Chap 4 of my book .

• Computational Biology: gene expression, cancer immunology, single-cell analysis, statistical genetics
• Computational Biology: sequence and structural analyses; Bayesian evolutionary modeling.