The JSB lab is led by the PI, Dr. Jingyi Jessica Li, with around eight 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. 

Li, W.V.*, Zhao, A., Zhang, S., and Li, J.J.* (2018). MSIQ: joint modeling of multiple RNA-seq samples for accurate isoform quantification. Annals of Applied Statistics in press.
Tong, X.*, Feng, Y.*, and Li, J.J. (2018). Neyman-Pearson (NP) classification algorithms and NP receiver operating characteristics (NP-ROC). Science Advances in press.