Changhu obtained his Ph.D. in 2024 from the School of Mathematical Sciences, Peking University. Prior to that, he received his B.S. degree from the School of Mathematics and Statistics, Northeast Normal University. His research interests include false discovery rate (FDR) control, post-clustering inference, post-selection inference, feature selection for clustering analysis, and their applications to biological data. He is currently working on developing high-power and efficient FDR control methods in high-dimensional settings.

 

 

Wang, C., Ge, X., Song, D., and Li, J.J. (2025). Comment on “Data Fission: Splitting a Single Data Point”—Data Fission for Unsupervised Learning: A Discussion on Post-Clustering Inference and the Challenges of Debiasing. Journal of the American Statistical Association 120(549):174–175.

 

Wang, C., Zhang, Z., and Li, J.J. (2025). SyNPar: Synthetic null data parallelism for high-power false discovery rate control in high-dimensional variable selection. arXiv.

 

Wang, C., Guo, J., Ma, Y., and Zheng, S. (2025). Kaiser Criterion in Factor Models. Acta Mathematica Sinica, English Series, 41(2), 547-552. 

 

Chen, Z., Wang, C., Huang, S., Shi, Y., and Xi, R. (2024). Directly selecting cell-type marker genes for single-cell clustering analyses. Cell Reports Methods, 4(7).

 

Wu, H., Wang, C., Xu, F., Xue, J., Chen, C., Hua, X. S., and Luo, X. (2024). Pure: Prompt evolution with graph ode for out-of-distribution fluid dynamics modeling. Advances in Neural Information Processing Systems, 37, 104965-104994.

 

Tang, J., Wang, C., Xiao, F., and Xi, R. (2024). Single‐cell gene regulatory network analysis for mixed cell populations. Quantitative Biology, 12(4), 375-388.

 

Wang, C., Chen, Z., and Xi, R. (2023). Feature screening for clustering analysis. arXiv preprint arXiv:2306.12671.