1). Mining Omics Signatures for Health and Disease
2). Integrating Multi-Omics for a Comprehensive Understanding
3). Omics Analysis in Therapeutic Areas
Our lab develops statistical, computational, and AI methods for complex omics data, especially microbiome studies, enabling robust analysis, discovery, and clinical translation across diverse diseases.
Our laboratory develops and applies innovative statistical, computational, and artificial intelligence methods to advance the analysis of complex omics data, with a particular focus on microbiome and metagenomic studies. We develop tools that span the full analytical pipeline, from upstream data preprocessing and integration to downstream quantitative analysis and disease prediction.
Our work translates statistical innovation into biomedical discoveries and clinical applications across diverse areas, including cardiovascular disease, aging-related conditions, organ transplantation, cancer, and women’s health. By reducing bias and enhancing sensitivity, our data-driven approaches enable reliable discovery and informed decision-making, accelerating the translation of findings from bench to bedside.
Biography
Dr. Ling's research focuses on the development and application of robust and powerful quantile regression methods and machine learning/deep learning approaches for complex and structured biomedical data, particularly in omics studies.
Distinctions:
R01 grant as PI
W. J. Youden Award in Interlaboratory Testing from American Statistical Association
Joseph L. Fleiss Prize for Outstanding Dissertation from Columbia University
Distinguished Student Paper Award from Eastern North American Region International Biometric Society
