The JSB lab is led by the PI, Dr. Jingyi Jessica Li, with around ten highly motivated graduate and undergraduate students. Our research is at the junction of statistics and biology, as our lab name JSB represents. We focus on developing statistical and computational methods motivated by important questions in biomedical sciences and abundant information in big genomic and health related data. On the statistical methodology side, our example interests include association measures, high-dimensional variable selection, and classification metrics. On the biomedical application side, our example interests include next-generation RNA sequencing, comparative genomics, and information flow in the central dogma. 

If you are interested in our research, please check out our YouTube channelTwitter, and Medium.

69. Song, D., Wang, Q., Yan, G., Liu, T., and Li, J.J. (2023). scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics. Nature Biotechnology[ SOFTWARE ] | [ PDF ]
64. Zhou, H.J., Li, L., Li, Y., Li, W., and Li, J.J. (2022). PCA outperforms popular hidden variable inference methods for QTL mapping. Genome Biology 23:210. [ Highlight talk at RECOMB 2023 ] [ SOFTWARE ] | [ PDF ]
59. Li, Y.*, Ge, X.*, Peng, F., Li, W., and Li, J.J. (2022). Exaggerated false positives by popular differential expression methods when analyzing human population samples. Genome Biology 23:79. [ UCLA NEWS ] [ CODE ] | [ PDF ]