معرفی
Tao Wang is an Assistant Professor Adjunct in the Department of Biostatistics at Yale School of Public Health and a tenure-track Associate Professor and Principal Investigator in the SJTU-Yale Joint Center for Biostatistics and Data Science at Shanghai Jiao Tong University. His work focuses on statistical methods for high-dimensional biomedical data, especially dimension reduction, microbiome analysis, and integrative genomic modeling.
Education:
- PhD in Statistics, Hong Kong Baptist University, 2013
- MS, East China Normal University, 2010
Research Interests:
Dr. Wang develops statistical and computational techniques to tackle complex, high-dimensional data arising from modern biotechnologies. His methodological portfolio includes adaptive independence tests for microbiome community data, penalized prediction models for sequencing counts, and counting-process-based dimension reduction for censored survival outcomes. He also works on Bayesian tree-structured models linking dietary nutrients to gut microorganisms and joint estimation of Gaussian graphical models over spatial and temporal domains. These interests sit at the intersection of biostatistics, bioinformatics, machine learning, and epidemiology, with applications spanning the human microbiome, epigenomics, and large-scale cohort studies.
Publications at a Glance:
Across six major papers (2015-2019) in Biometrics and Biometrika, Wang has consistently advanced methodology for high-dimensional count, compositional, and censored data. Themes include adaptive testing, predictive modeling, dimension reduction, and network estimation, often motivated by collaborative projects in genomics and microbiome science.
Honours:
- Elected Member, International Statistical Institute (2020)
Collaboration & Advising:
He maintains an active collaboration with Yale’s Dr. Hongyu Zhao, co-authoring all showcased publications. No formal advisees are listed in the provided material.
Laboratories & Teams:
He leads research activities within the SJTU-Yale Joint Center for Biostatistics and Data Science, integrating resources from both Shanghai Jiao Tong University and Yale to advance data-intensive biomedical science.
