Huilin Li's Lab--Statistical and Computational Methods for Multi-omics and Human Health 

Welcome to the Huilin's Lab!  I am a full Professor in Division of Biostatistics, Department of Population Health, New York University School of Medicine, and Director of the NYU Multi-Omics Study Design and Data Integration Resource(MOSDIR). In this role, I lead the development and application of rigorous, biologically informed multi-omics analytical frameworks, with expertise spanning study design, data processing, statistical modeling, and integrative analysis. Through MOSDIR, I have established a collaborative infrastructure that bridges diverse multi-omics research efforts at NYU Langone, enabling mechanistic investigations across a broad range of health and disease areas.

My research focuses on methodological development and translational applications for integrative, multi-modal multi-omics data, including genomics, metabolomics, microbiome, proteomics, transcriptomics, clinical informatics, nutritional data, and wearable data, within longitudinal clinical, observational, and experimental studies. Specifically, I develop:

1. Multi-modal data integration approaches to characterize environment–microbe–host interactions for biomarker discovery, disease subtyping, risk stratification, and prediction; and

2.  Biologically guided integration methods to elucidate causal mediation pathways underlying disease mechanisms.

Since 2013, I have served as PI on multiple NIH R01 and U01 methodological grants (R01 LM014085, R01 DK110014, U01 CA170948), leading the development of novel analytical methods spanning upstream bioinformatics to downstream biostatistical modeling for high-dimensional longitudinal data. My group has developed widely used analytic pipelines for metagenomic and metatranscriptomic data processing (e.g., LongStrain, STEMSIM), omics biomarker discovery (e.g., MRS, OMiAT, massMap), longitudinal dynamic modeling (e.g., MTA, JointMM, ARZIMM), and causal/mediation analysis (e.g., SparseMCMM). Collectively, these tools enable robust, scalable, and biologically informed analysis of complex multi-omics data and have been applied across diverse biomedical studies.

In parallel, I have made substantial contributions as a collaborator with clinical and translational investigators in cancer, infectious diseases, diabetes, obesity, and intervention studies. In these collaborations, I lead the integration of study design and analytic strategy to ensure rigor, reproducibility, and biological interpretability. My team works closely with domain scientists to align methodological innovation with practical study needs, thereby maximizing the impact of multi-omics data in understanding disease mechanisms and informing clinical research. 

    Mailing address:

    Department of Population Health

    Division of Biostatistics

    New York University School of Medicine

    180 Madison Ave, Fourth fl, rm 453

    New York, New York 10016

    Tel: 646-501-3651 

    E-mail: huilin.li @nyulangone.org