Jingyi Jessica Li

Dr. Jingyi Jessica Li is a Professor in the Department of Biostatistics, UCLA Fielding School of Public Health. Her specific appointments are 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.

Before 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 genomics and health-related data. On the statistical methodology side, her research interests include association measures, asymmetric classification, multiple testing, 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. To bridge statistics and biology, Jessica is interested in enhancing the rigor in genomics data analysis.

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), the MIT Technology Review 35 Innovators Under 35 China (2020), the Harvard Radcliffe Fellowship (2022), and the COPSS Emerging Leader Award (2023).

Education

  • PhD, Biostatistics, University of California, Berkeley, CA
  • BS, Biological Sciences, Tsinghua University, Beijing, China

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Areas of Interest

Dr. Li’s research is at the interface between statistics and biology. Her 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 she has examined include:

Bioinformatics / Statistical Genomics:

  • Enhancing the rigor of data analysis
  • Development of realistic generative models
  • 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

Statistics:

  • 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

Selected Publications

Google Scholar Profile 

 

Statistical rigor in omics data analysis

 

Single-cell RNA-seq

 

Bulk RNA-seq isoform discovery and quantification

 

Central dogma and translational control

 

Classification methodologies and applications

 

Microbiome sequencing data imputation

 

Networks

 

High-dimensional model inference

 

Comparative genomics 

 

Gene regulation