The Environmental Determinants of Diabetes in the Young (TEDDY) study is searching for factors influencing the development of type 1 diabetes (T1D) in children. Research has shown that there are certain genes that correlate to higher risk of developing T1D, but not all children with these genes...

The Diabetes Autoimmunity Study in the Young (DAISY) seeks to find environmental factors that can trigger the development of type 1 diabetes (T1D) in children. DAISY follows children with high-risk of developing T1D based on family history or genetic markers. Genes, diets, infections, and...

Machine learning is a core technology that is rapidly advancing within type 1 diabetes (T1D) research. Our Human Islet Research Network (HIRN) grant is studying early cellular response initiating β cell stress in T1D through the generation of heterogenous low- and high-throughput molecular...

Short Biography Caroline (Carrie) Harwood received her Ph.D. in microbiology from the University of Massachusetts and completed postdoctoral work at Yale University. She held academic appointments at Cornell University and the University of Iowa before moving to the University of Washington in 2005...

Sara Gosline received BA in Computer Science from Columbia University and spent two years working in software before returning to graduate school full time. She received her Masters and PhD in Computer Science from McGill University with a specialty in Bioinformatics and then moved to the...

Earth Scientist Emily Graham is an quantitative ecosystem ecologist in the Biological Sciences Group at PNNL and is part of the Ecosystem Science Team. She is co-principal investigator of the Subsurface Biogeochemical Research Science Focus Area (SFA) and a key member of PNNL’s soil microbiome SFA...

Biography Kelly is a senior data scientist in the Computational Biology group within the Biological Sciences Division at Pacific Northwest National Laboratory (PNNL). After earning a MS in Biostatistics from the University of Washington in 2012, she worked at a cancer research company for two years...

David Degnan is a biological data scientist who develops bioinformatic and statistical pipelines for multi-omics data, specifically the fields of proteomics, metabolomics, and multi-omics (phenotypic) data integration. He has experience with top-down & bottom-up proteomics analysis, genomics &...