Research

Research areas, projects, and workshops from the Feng Lab, transfer learning, Neyman-Pearson classification, variable screening, network analysis, and applications.

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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 (\(p \gg n\)).

Workshops organized

Statistical Network Analysis and Beyond 2025 (SNAB 2025)

June 2–4, 2025 · Hotel New Otani, Tokyo, Japan

Co-organized with Jiashun Jin · Qingfeng Liu · Maggie Niu · Ji Zhu