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Through bioinformatics and artificial intelligence (AI), we can use this omic data to create multi-dimensional models of the whole hierarchical system in healthy, diseased, and treated states.

Multi-omics data processing with AI

Team leaders


Assistant Professor, Computer & Information Science & Engineering

Kiley Graim, PhD

Dr. Graim’s research operates at the confluence of life science research and computer science. Her lab develops machine learning models that integrate diverse large-scale genomics data to address key questions in human health and disease. These models effectively probe complex biological networks to answer questions arising from basic science and translational research. Dr. Graim’s overarching goal is to map the mechanisms of human diseases and to enable development of personalized therapies.


Assistant Professor, Department of Biostatistics

Rhonda Bacher, PhD

Her research focuses on the development of statistical methods and computational tools for analyzing high-throughput genomic and next-generation sequencing data. My current focus is on single-cell transcriptomics and epigenomics and time-course transcriptomics.