Projects

Research projects from the Feng Lab across transfer learning, Neyman-Pearson classification, variable screening, social network analysis, and more.

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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.
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Neyman-Pearson Classification

Statistical learning frameworks for asymmetric error control, prioritizing type I error control over overall accuracy.
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Social Network Analysis

Statistical methods for community detection, multi-layer networks, network covariates, and longitudinal network modeling
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High-dimensional Variable Screening

Statistical methodologies for identifying important features in ultra-high dimensional data (\(p \gg n\)).