Pei Wang, PI
I am currently a Professor of Genetics and Genomic Sciences. I was born in Wuhan, China. I obtained my B.S. in Mathematics from Peking University, China, in 2000. I then pursued my graduate study in the U.S. and received my Ph.D. in Statistics from Stanford University in 2004. Between 2004-2013, I served as a faculty at Fred Hutchinson Cancer Research Center and University of Washington, Seattle, WA. In Oct 2013, I joined Icahn Medical School at Mount Sinai, New York.
Francesca Petralia, Ph.D
is an Assistant Professor in the Icahn Institute of Genomics and Multiscale Biology at Mount Sinai Medical School. She received her Ph.D. in Statistics from Duke University in 2013 where she has developed clustering algorithms based on Bayesian mixture models and Bayesian non-parametric models for conditional density estimation. After joining Mount Sinai, she has focused on omic data analysis and developed novel algorithms for i) pathway analysis ii) gene regulatory network inference via the integration of disparate genomic data and iii) the simultaneous estimation of multiple related networks such as protein-protein and gene-gene networks.
Weiping Ma, Ph.D
Weiping Ma obtained his Ph.D at the School of Mathematical Sciences at Fudan University in Shanghai, China. His work focused on theories of longitudinal and functional data analysis with applications in environmental epidemiology and biomedical science. After completing his graduate work in 2012, he joined the School of Public Health at Fudan University as a Postdoctoral Fellow working on modeling time series data in epidemiology. He joined Mount Sinai in 2014 under the advisement of Dr. Pei Wang and has been focused on integration analysis of multi-omics data using penalized multivariate mixed effect model, developing novel imputation algorithms for proteomics data, and statistical analysis on digital health data.
Nicole Tignor, Ph.D
is a senior scientist of the Digital Health Center at the Icahn School of Medicine at Mount Sinai. She obtained her BA in Physics from the University of Chicago. She has a PhD in Ecology and Evolutionary Biology from Stony Brook University where she worked in the area of evolutionary genomics. She did her postdoctoral work in the area of statistical genetics. Since joining Mount Sinai, she has worked on projects in epigenetics and digital health. Together with other team members, she has developed a collection of methods for analyzing data from mobile health apps.
Shrabanti Chowdhury, Ph.D
is a Postdoctoral fellow in the Department of Genetics and Genomic Sciences at Icahn School of Medicine at Mount Sinai. She received her Ph.D in applied Statistics from University of California, Riverside in 2016. Her dissertation was focused on the design of factorial experiments. After graduation, she joined Wayne State University School of Medicine as a research fellow, where her research focused on developing novel statistical methodologies in clinical trials using a Bayesian hierarchical modeling approach. She joined Mount Sinai in 2018 under the supervision of Dr. Pei Wang and has been focusing on integrative omics data analysis including 1) imputation of proteomics data 2) pathway enrichment analysis 3) gene regulatory network inference, among others. Her current research is focused on developing novel algorithms for learning directed networks based on time-course proteomics data.
is a software developer focused on creating intuitive, user-friendly data visualizations.