Yusha Liu, PhD

About

Dr. Liu received her PhD in Statistics from Rice University in 2019 and postdoctoral training from the Department of Human Genetics at the University of Chicago prior to joining UNC. Dr. Liu’s research interests lie at the intersection of statistics and biology, and she is particularly interested in developing and applying flexible and scalable statistical approaches to analyze large-scale and complex genomics data, such as single cell data, and ultimately contributing to the understanding of complex diseases like cancer and the development of targeted therapy and prevention strategies.

Honors and Awards

Ralph Budd Best Engineering Ph.D. Thesis Award (awarded to one doctoral student for the best-written thesis in the School of Engineering)
2020, Rice University

Distinguished Student Paper Award
2020, ENAR, International Biometric Society

Finalist of Student Paper Competition, Nonparametric Statistics Section, JSM
2020, American Statistical Association

Student Paper Award, Section of Bayesian Statistical Science, JSM
2019, American Statistical Association

Research Activities

Single-cell data modeling

Omics data integration

Bayesian inference 

Cancer research

Functional data analysis

Biomarker-driven clinical trial design

Quantile regression

Key Publications

Dissecting tumor transcriptional heterogeneity from single-cell RNA-seq data by generalized binary covariance decomposition. Liu, Y., Carbonetto, P., Willwerscheid, J., Oakes, S.A., Macleod, K.F. and Stephens, M. (2025). Nature Genetics , 57.
View publication

A flexible model for correlated count data, with application to analysis of gene expression differences in multi-condition experiments. Liu, Y., Carbonetto, P., Takahama, M., Gruenbaum, A., Xie, D., Chevrier, N. and Stephens, M.  (2024). The Annals of Applied Statistics, 18(3), 2551-2575.
View publication

On function-on-scalar quantile regression. Liu, Y., Li, M. and Morris, J.S.
View publication

Bayesian adaptive trial design for a continuous biomarker with possibly nonlinear or nonmonotone prognostic or predictive effects. . Liu, Y., Kairalla, J.A. and Renfro, L.A. (2022). Biometrics, 78(4), 1441-1453.

Function-on-scalar quantile regression with application to mass spectrometry proteomics data. . Liu, Y., Li, M. and Morris, J.S.  (2020). The Annals of Applied Statistics, 14(2), 521-541.

Education