Jingyi Li

Jingyi Jessica Li*


8951 Math Sciences Bldg.


Professor, Biostatistics, Statistics, Human Genetics
Member, Bioinformatics GPB Home Area, Genetics & Genomics GPB Home Area

Research Interests

My research is at the interface between statistics and biology. My primary research interest lies in developing new statistical methods for understanding biological questions, especially those related to large-scale genomic and transcriptomic data.  The specific topics I have examined include

Bioinformatics / Statistical Genomics:

Statistical methods for analyzing next-generation bulk and single-cell RNA sequencing data

Using statistics to quantitate the Central Dogma, a fundamental principle in molecular biology

Comparative genomics: developing novel statistical methods to investigate conserved or divergent biological phenomena in different tissue and cell types across multiple species

Novel statistical methods for imputing missing data or extracting hidden information from various types of genomics data

Identification of gene-gene co-expression and protein-DNA and protein-RNA interactions using diverse genomic data


Measures of association

Neyman-Pearson classification that controls the prioritized type of error in binary classification

High-dimensional linear model inference and variable selection

Community detection in a bipartite network with node covariates

P-value free control of false discovery rates

Labeling ambiguity issue in multi-class classification


Jingyi Jessica Li is a Professor in the Department of Statistics (primary) and the Departments of Biostatistics, Computational Medicine, and Human Genetics (secondary) at University of California, Los Angeles (UCLA). She is also a faculty member in the Interdepartmental Ph.D. Program in Bioinformatics. Prior to joining UCLA in 2013, Jessica obtained her Ph.D. degree from the Interdepartmental Group in Biostatistics at University of California, Berkeley, where she worked with Profs. Peter J. Bickel and Haiyan Huang. Jessica received her B.S. (summa cum laude) from the Department of Biological Sciences and Technology at Tsinghua University, China in 2007. Jessica and her students 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, her research interests include association measures, asymmetric classification, and high-dimensional variable selection. On the biomedical application side, her research interests include bulk and single-cell RNA sequencing, comparative genomics, and information flow in the central dogma. Jessica is the recipient of the Hellman Fellowship (2015), the PhRMA Foundation Research Starter Grant in Informatics (2017), the Alfred P. Sloan Research Fellowship (2018), the Johnson & Johnson WiSTEM2D Math Scholar Award (2018), the NSF CAREER Award (2019), the UCLA DGSOM Keck W. M. Keck Foundation Junior Faculty Award (2020), and the MIT Technology Review 35 Innovators Under 35 China (2020).


A selected list of publications: