Prof. Aizhen Ren research work centers on machine learning, deep learning, statistical inference, and their applications in economics, finance, and computational biology. A significant portion of the research contributes to the development and mathematical validation of advanced bootstrap techniques, including the speedy double bootstrap method, which enhances the statistical reliability of phylogenetic tree estimation and provides third-order accurate unbiased p-values. These methods have been applied to evolutionary analyses of horse breeds, supporting biological and genomic investigations with high-precision statistical tools. In the financial domain, the research explores machine-learning-based trend prediction models, such as multiscale bootstrap-corrected random forest voting systems used to forecast stock index movement with improved accuracy and inference reliability. Additional work includes the construction of financial risk early-warning models for listed companies using multiple machine learning approaches, reflecting an interdisciplinary blend of statistics, computing, and economics. Contributions also extend to consumption behavior analysis employing regression-based models, as well as deep learning ensemble frameworks integrating empirical mode decomposition and temporal convolutional networks for time-series prediction tasks. The released R package SDBP operationalizes the novel bootstrap methodology, enabling researchers to compute unbiased p-values efficiently. Overall, the research advances methodological innovation and practical applications across data-intensive scientific domains.