The Department’s research expertise and focus encompass several areas, including: (1) Computational Statistics, which covers topics such as Bayesian hierarchical models, empirical Bayes, Markov chain Monte Carlo, statistical machine learning, and large-scale data analysis; (2) Functional Data Analysis, which includes Bayesian inverse problems in physical oceanography, trajectory analysis in life course epidemiology, and oceanography; (3) Inferential Statistics, which involves efficient estimation for semiparametric models, model selection, empirical likelihood, knockoff statistics, non-standard asymptotic theory, and post-selection inference; (4) Statistical Genetics Genomics Omics, which covers the analysis of whole-genome sequencing data and integrative analysis of multi-omics data; (5) Survival and Event History Analysis, which includes competing risks models for HIV/AIDS data, network survival models, counting process, and martingale methods; (6) Time Series Analysis, which covers inference for stochastic processes, statistical finance, and risk management; (7) Causal Inference, which deals with methods for identifying causal relationships from observational or experimental data, with applications in medical research; (8) Spatial Statistics, which focuses on the analysis of data with spatial or spatio-temporal structure, including applications in epidemiology and public health; and (9) Deep Learning, which involves the development of algorithms and models for learning from complex and high-dimensional data, with applications in medical imaging, diagnosis, and treatment.