Research Interests
Statistical machine learning,
(Hyper)graph network analysis,
Embedding structured data, latent space models,
Uncertainty quantification (inference), high-dimensional statistics
Journal Publication
Supervised homogeneity fusion: a combinatorial approach.
W. Wang*, S. Wu*, Z. Zhu, L. Zhou, P.X.K. Song. The Annals of Statistics, 52(1): 285-310, 2024.
On sure early selection of the best subset.
Z. Zhu, S. Wu. arXiv.2107.06939. IEEE Transactions on Information Theory, 70(12): 8870-8891, 2024.
A distributed community detection algorithm for large scale networks under stochastic block models.
S. Wu*, Z. Li*, X. Zhu. Computational Statistics and Data Analysis, vol. 187, 2023.
Preprints
ReLaSH: Reconstructing joint latent spaces for efficient generation of synthetic hypergraphs with hyperlink attributes.
F. Ma, S. Wu, G. Xu, J. Zhu.
Efficient synthetic network generation via latent embedding reconstruction.
F. Jiang, Y. Bu, S. Wu, G. Xu, J. Zhu.
Denoising diffused embeddings: a generative approach for hypergraphs.
S. Wu, J. Yang, G. Xu, J. Zhu. arXiv.2501.01541.
Latent space directed counting network models with node covariates for citation exchange between statistical journals.
S. Wu, T. Zhang, G. Xu, J. Zhu.
A general latent embedding approach for modeling non-uniform high-dimensional sparse hypergraphs with multiplicity.
S. Wu, G. Xu, J. Zhu. arXiv.2410.12108.
Statistical inference on latent space models for network data.
J. Li, S. Wu, C. Cui, G. Xu, J. Zhu. arXiv.2312.06605.
*: (co-) first author / equal contribution