Transfer Learning and Multi-task Learning Statistical learning theory and methods for transfer and multi-task learning, including high-dimensional, unsupervised, federated, and privacy-preserving settings. Neyman-Pearson Classification Statistical learning frameworks for asymmetric error control, prioritizing type I error control over overall accuracy. Social Network Analysis Statistical methods for community detection, multi-layer networks, network covariates, and longitudinal network modeling High-dimensional Variable Screening Statistical methodologies for identifying important features in ultra-high dimensional data (\(p \gg n\)).
