Holder： Huilin Li(New York University)
Time：2025-12-09 14:00-15:00
Location：Conference Room 220, New Public Health Building, Peking University Health Science Center
Abstract:
Over the past two decades, my research has been shaped by the interplay between collaborative biomedical projects and the development of new statistical methodology. Working closely with investigators in medicine and public health, I have often found that scientific questions emerging from collaborative studies highlight critical methodological gaps, which in turn inspire new analytic approaches. In this talk, I will reflect on this bidirectional process and share examples from my own trajectory. I will first highlight work on microbiome association analysis, microbial mediation analysis, and methods for longitudinal microbiome data, illustrating how challenges in real studies motivated the methodological development. I will then discuss the natural transition of these ideas into the broader domain of multi-omics integration, where complexity and heterogeneity across data types raise new statistical questions. Throughout, I will emphasize lessons learned about building bridges between collaborative biomedical research and methodological innovation, and how such integration can both advance science and shape a sustainable research career.
About the Speaker:
Huilin Li, PhD, is a Professor in the Department of Population Health at NYU and Director of the Biostatistics Resource and the Multi-omics Study Design and Data Integration Resource. She is a Senior Editor for mSystems and a standing member of the NIH Kidney, Endocrine, and Digestive Disorders Study Section. Dr. Huilin Li specializes in integrative multi-omics research—including metabolomics, microbiome, genomics, transcriptomics, and clinical informatics—with extensive experience leading prospective longitudinal and experimental studies.
李惠琳博士是纽约大学人口健康系教授，同时担任生物统计资源中心及多组学研究设计与数据整合资源中心主任。她是 mSystems 期刊的高级编辑，并长期担任美国国立卫生研究院（NIH）肾脏、内分泌和消化疾病评审组的常任委员。李惠琳博士专长于整合式多组学研究，包括代谢组、微生物组、基因组、转录组及临床信息学等，并在前瞻性纵向与实验性研究设计方面具有丰富经验。
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