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 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.