Research Interests
Statistical machine learning,
(Hyper)graph network analysis,
Generative modeling for structured data,
Uncertainty quantification, 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] [GitHub] 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.
Under Revision
Denoising diffused embeddings: a generative approach for hypergraphs.
S. Wu, J. Yang, G. Xu, J. Zhu. [arXiv] Under major revision at Journal of the American Statistical Association.
A general latent embedding approach for modeling non-uniform high-dimensional sparse hypergraphs with multiplicity.
S. Wu, G. Xu, J. Zhu. [arXiv] [GitHub] Under major revision at Journal of the American Statistical Association.
Statistical inference on latent space models for network data.
J. Li*, S. Wu*, C. Cui, G. Xu, J. Zhu. [arXiv] Under major revision at the Annals of Statistics.
Under Review
Latent space directed counting network models with node covariates for citation exchange between statistical journals.
S. Wu, T. Zhang, G. Xu, J. Zhu. Submitted.
ReLaSH: Reconstructing joint latent spaces for efficient generation of synthetic hypergraphs with hyperlink attributes.
F. Ma*, S. Wu*, G. Xu, J. Zhu. Submitted.
Efficient synthetic network generation via latent embedding reconstruction.
F. Jiang, Y. Bu, S. Wu, G. Xu, J. Zhu. Submitted.
*: (co-) first author / equal contribution