My central research focus is to develop and apply innovative statistical methods to solve challenging data analysis problems in three biomedical research areas: 1) high-throughput metagenomics/microbiome/genetics data analysis, 2) complex survey methodology, and 3) biomedical collaborative research.
I am the director of Metagenomic/Microbiomic Data Analysis Group in Department of Population health. With NIH’s support, my research group has been focused on developing novel statistical methods in analyzing metagenomics and microbiome data, including microbiome association test, longitudinal microbiome data analysis, and microbiome causal/mediation modeling. We work closely with various NYULMC research labs to insure that the latest statistical methods are incorporated for optimal experimental design and downstream data analysis. We develop best practice data analysis pipelines for a variety of experimental designs that integrate proprietary software from the existing microbiome data analysis platform and the best Open Source tools, as well as software developed within our group.
Associate Professor, Department of Population Health
Associate Professor, Department of Environmental Medicine
PhD from University of Maryland
National Cancer Institute, Biostatistics Branch, Division of Cancer Epidemiology and Genetics
Microbiome. 2017 Apr 24; 5(1):45-45
Genetic epidemilogy. 2016 Nov; 40(7):579-590
MBio. 2018 Jun 19; 9(3):
IS THERE A MINIMUM SELF-MONITORING FREQUENCY FOR EFFECTIVE WEIGHT LOSS? [Meeting Abstract]
Annals of behavioral medicine. 2018 APR; 52:S731-S731
Journal of biological chemistry. 2018 Mar 30; 293(13):4713-4723
BMC genomics. 2018 Mar 20; 19(1):210-210
Journal of clinical investigation. 2018 Jan 16; 128(2):682-698
Genetic epidemilogy. 2017 Sep 5; 41(8):769-778