Research Group - Biostatistics

This group develops advanced statistical techniques to analyze complex biomedical data, including methods for longitudinal studies, survival analysis, and Bayesian modeling. Their work supports a wide range of applications in healthcare, clinical research, and epidemiology, improving data interpretation and decision-making in medical contexts.

Themes: 

• Bayesian Methods
• Survival Analysis
• Longitudinal Data Analysis
• Mixed Models
• High-Dimensional Data
• Missing Data Imputation
• Meta-Analysis
• Variable Selection & Dimension Reduction

• Edward Bedrick - Analysis of observational data; Bayesian methods; Generalized linear and mixed models
• Chiu-Hsieh Paul Hsu - Survival analysis; Missing data; Statistical modeling
• Chengcheng Hu - High-dimensional data; Survival analysis; Longitudinal data; Missing data; Measurement error
• Lifeng Lin - statistical methods for meta-analysis, network meta-analysis of multiple-treatment comparisons, publication bias, and Bayesian methods. He is also interested in the applications of statistical methods to real-world problems
• Yiwen Liu - Dimension reduction and variable selection, big data analytics, data integration
• Xiaoxiao Sun - Medical Imaging, Nonparamatric Modelling, Computational Biology
• Walter Piegorsch - Data analytics; Genomics; Environmental statistics; Risk assessment; History of statistics

This group develops computational and statistical tools to analyze high-throughput biological data, with a focus on genomics, transcriptomics, and precision medicine. Their research advances our understanding of disease mechanisms and supports biomedical discovery through bioinformatics, single-cell analysis, and integrative data science.

Themes: 

• Genomics and transcriptomics
• Single-cell and nanopore sequencing
• Bioinformatics and clinical informatics
• Precision medicine and drug discovery
• Statistical genetics
• Biomedical big data

• Hongxu Ding - Single-cell analysis, nanopore sequencing. His lab develops computational biology approaches to interpret single-cell omics profiles and nanopore sequencing readouts.
• Jingjing Liang - Statistical genetics and genomics, including computational methods for analyzing large-scale sequencing data, rare variant association analysis, genomics-driven drug target discovery and precision medicine
• Haiquan Li - Biomedical big data science, Translational bioinformatics, bioinformatics, clinical informatics.
• Mingyu Liang - molecular systems medicine. e. The current work in our group focuses on three areas: (epi)genomics and precision medicine, regulatory RNA, and cellular metabolism, as they relate to hypertension, cardiovascular and kidney disease.
• Travis Wheeler - Pharmacy Practice and Science 

This group designs and analyzes studies to understand health outcomes, treatment effects, and disease risk. Their work spans clinical trials, observational studies, and biomarker development, with expertise in study design, longitudinal data, and high-dimensional biological measurements.

Themes: 

• Clinical trial design and analysis
• Epidemiological methods
• Longitudinal data analysis
• Risk prediction and assessment

• Zhao Chen - Research study design; Longitudinal data analysis; Risk assessment.
• Denise Roe - Biostatistics. Clinical trials; Epidemiological studies; Pharmacokinetics.
• Dean Billheimer - Measurement and normalization, Quantitative proteomics, Statistical methods for compositional data.