Presentations
“I strive to build methods that are interpretable, reproducible, and grounded in scientific reality—advancing data-driven discovery without compromising rigor.”
— Dr. Jingyi Jessica Li
/content/dam/www/people-profile-photos/l/jingyi-jessica-li/jingyi-jessica-li-d.jpg
Li
Jingyi Jessica Li, PhD
- Professor and Program Head, Biostatistics Program, Public Health Sciences Division, Fred Hutch
- Professor, Herbold Computational Biology Program, Public Health Sciences Division, Fred Hutch
- Member, Translational Data Science Integrated Research Center (TDS IRC), Fred Hutch
- Donald and Janet K. Guthrie Endowed Chair in Statistics, Fred Hutch
- Affiliate Professor, Department of Biostatistics, University of Washington
Background
Dr. Jingyi Jessica Li is head of the Biostatistics Program. A leading statistician working at the interface of statistics and biology, Li develops reliable and interpretable statistical methods to analyze complex biological data, with a central focus on understanding how genes function and are regulated in health and disease. Her research emphasizes statistical rigor, aiming to uncover hidden patterns in gene expression while ensuring that discoveries are trustworthy — even in the presence of noise, bias or limited data quality. Li has developed statistical methods for analyzing bulk, single-cell, and spatial transcriptomics data; generating realistic synthetic data for benchmarking methods and as controls to improve the reliability of analysis (e.g., controlling false discovery rates); and advancing statistical learning to handle imperfect data (e.g., labeling ambiguity), reflect user-defined priorities (e.g., emphasizing certain outcomes), and enhance power and efficiency in high-dimensional variable selection. Notably, she has used statistics to quantify the central dogma — a fundamental principle in molecular biology — and demonstrated the dominant role of transcription over translation in determining protein abundances.
Professor, Statistics and Data Science (on leave 2025–2026), University of California, Los Angeles
Education
PhD, Biostatistics, University of California, Berkeley, 2013
BS, Biological Sciences, Tsinghua University, 2007
Awards & Honors
Mortimer Spiegelman Award, American Public Health Association (APHA), 2025
Guggenheim Fellowship, John Simon Guggenheim Memorial Foundation, 2025
Overton Prize, International Society for Computational Biology (ISCB), 2023
Emerging Leader Award, Committee of Presidents of Statistical Societies (COPSS), 2023
Radcliffe Fellowship, Radcliffe Institute for Advanced Study at Harvard University, 2022
MIT Technology Review 35 Innovators Under 35, China, 2020
CAREER Award, National Science Foundation, 2019
Math Scholar Award, Johnson & Johnson Women in STEM2D, 2018
Sloan Research Fellowship, Alfred P. Sloan Foundation, 2018
Hellman Fellow, Hellman Foundation, 2015
Faculty Career Development Award, UCLA, 2015
Outstanding Graduate Student Instructor Award, UC Berkeley, 2010
Outstanding Graduate, Tsinghua University (highest graduation honor; one student per department; roughly equivalent to summa cum laude), 2007
Current Projects
Our research uses synthetic data to make hidden statistical hypotheses explicit—bypassing traditional derivations and enabling transparent, reproducible inference in complex data settings. The Junction of Statistics and Biology (JSB Lab)
Current Funding
U24 HG011735 (Subaward PI)
NHGRI Genome Technology Program Opportunity Fund
“Statistical methods for enhancing the rigor of metacell partitioning in single-cell multi-omics data”
R01 HG014687 (PI)
NHGRI
“Experimental-data-based in-silico data generation platform to improve the accuracy and reliability of single-cell and spatial omics data analysis”
R35 GM140888 (PI)
NIGMS
“Statistical methods for elucidating regulatory mechanisms and functional impacts of transcriptome variation at population and single-cell scales”