Jingyi Jessica Li

Jingyi Jessica Li (Chinese:李婧翌) is a statistical scientist whose work bridges statistics and computational biology, with a focus on developing rigorous statistical methods for the analysis of high-throughput biological data. Her research integrates statistical principles with biological data analysis, particularly in genomics and transcriptomics. She is currently a professor of Statistics, Biostatistics, Human genetics, Computational medicine, and Bioinformatics at the University of California, Los Angeles.

Jingyi Jessica Li
李婧翌
Born	1985
Alma mater	Tsinghua University (B.S.) University of California, Berkeley (Ph.D.)
Known for	Statistical methods for RNA sequencing Bioinformatics tools for single-cell transcriptomics Quantifying the central dogma using statistics P-value-free false discovery rate control Neyman-Pearson classification for medical diagnostics
Scientific career
Fields	Statistics Bioinformatics Computational biology Machine learning Genomics
Institutions	University of California, Los Angeles
Thesis	Statistical Methods for Analyzing High-throughput Biological Data  (2013)
Doctoral advisors	Peter J. Bickel Haiyan Huang
Website	jsb.ucla.edu

Li has won several awards, including the Overton Prize^[1] from the International Society for Computational Biology and the Emerging Leader Award^[2] from COPSS. In 2025, she was appointed to a Guggenheim Fellowship.^[3]

Education and career

Li started her undergraduate education at Tsinghua University in 2003. She moved to the University of California, Berkeley for her Ph.D., and then started as a faculty member at the University of California, Los Angeles in 2013.^[1] As of 2025 she is a full professor.^[4]

From 2022 to 2023, she was a Radcliffe Fellow at the Harvard Radcliffe Institute for Advanced Study and a visiting professor in the Department of Statistics at Harvard University.^[5]

Research

Her work relates to transcription and translational control of protein expression levels in the central dogma and statistical methods for RNA-seq data at the bulk and single-cell levels.

Her 2015 Science study, a reanalysis of a 2011 Nature article, suggested that transcription, rather than translation, remains the dominant factor regulating protein abundance, primarily influencing differences in protein expression levels across genes.^[6]

Her research group developed a suite of single-cell data simulators, including scDesign,^[7] scDesign2 that captures gene-gene correlations,^[8] scDesign3 for single-cell and spatial multi-omics data,^[9] and scReadSim for single-cell RNA-seq and ATAC-seq read simulation.^[10] Besides, her group developed scImpute,^[11] an imputation tool for missing gene expression values.

Her contributions also extend to statistical and computational methodologies, including Clipper,^[12] a p-value-free false discovery rate (FDR) control method; ITCA, a criterion for guiding the combination of ambiguous class labels in multiclass classification;^[13] and Neyman-Pearson classification, a framework for prioritizing the control of misclassification errors in critical classes.^[14][15]

Her recent efforts advocate for the importance of statistical rigor in genomics data analysis. In a recent study, she and co-authors raised a warning in using popular RNA-seq differential expression (DE) methods blindly without checking the underlying assumptions. For example, in population-scale human RNA-seq samples where the negative binomial assumption for each gene does not hold, popular methods relying on this assumption can lead to excessive false discoveries, while non-parametric tests such as the Wilcoxon rank-sum test gives more reliable results.^[16] Moreover, she developed scDEED,^[17] a statistical method leveraging permutation techniques to evaluate and optimize embeddings produced by t-SNE and UMAP. scDEED detects dubious embeddings that fail to preserve mid-range distances and refines t-SNE and UMAP hyperparameters.