I am an Assistant Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign. Previously, I was a postdoc researcher at the University of Pennsylvania. I received my Ph.D. in Statistics from the University of Wisconsin-Madison.
Our lab aims to bridge the theoretical and empirical boundary of modern statistics and machine learning methods (self-supervised learning, nonparametric and high-dimensional statistics) and advance the practice of statistics in biomedical applications (microbiome and imaging).
Additional Campus Affiliations
Affiliate, Personalized Nutrition Initiative, Carl R. Woese Institute for Genomic Biology
Wang, S. (2022). Robust differential abundance test in compositional data. Biometrika. https://doi.org/10.1093/biomet/asac029
Wang, S. (Accepted/In press). Self-supervised Metric Learning in Multi-View Data: A Downstream Task Perspective. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.2022.2057317
Wang, S., Cai, T. T., & Li, H. (2021). Hypothesis testing for phylogenetic composition: a minimum-cost flow perspective. Biometrika, 108(1), 17-36. [asaa061]. https://doi.org/10.1093/biomet/asaa061
Wang, S., & Yuan, M. (2021). Revisiting colocalization via optimal transport. Nature Computational Science, 1(3), 177-178. https://doi.org/10.1038/s43588-021-00046-7
Wang, S., Fan, J., Pocock, G., Arena, E. T., Eliceiri, K. W., & Yuan, M. (2021). Structured correlation detection with application to colocalization analysis in dual-channel fluorescence microscopic imaging. Statistica Sinica, 31(1), 333-360. https://doi.org/10.5705/ss.202018.0230