Rebecca Deek

Rebecca Deek is an Assistant Professor at the University of Pittsburgh in the Department of Biostatistics and Health Data Science. Her research focuses on statistical and computational methods for analyzing omics data, particularly metagenomics and multiomics integration, to address challenges arising from advanced sequencing technologies in medical studies.

• PhD in Biostatistics (University of Pennsylvania, 2023)
• MS in Biostatistics (Columbia University, 2018)
• BS in Biological Sciences Teaching (New Jersey Institute of Technology and Rutgers University, 2016)

Her work develops novel methodologies for analyzing microbial covariation networks using copula models with mixture margins, zero-inflated latent Dirichlet allocation, and deep learning applications to microbiome research. She collaborates with clinicians to study interactions between genetics, transcriptomics, proteomics, metabolomics, and metagenomics in human health.

Recent publications highlight her contributions to multiomics data integration, microbiome-specific statistical models, and network analysis techniques. She maintains open-source R packages like CoMiCoN and zinLDA for these methods.

Dr. Deek teaches courses including BIOST 2143: Applied Longitudinal and Clustered Data Analysis and BIOST2025: Biostatistics Seminar. Her research is supported by collaborations with clinicians and computational scientists, focusing on translating complex data into actionable health insights.

She actively contributes to open-source software development and maintains a personal website showcasing her work.