The Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship

Madeline Durkee

Eric and Wendy Schmidt AI in Science Postdoctoral Fellow



Cellular dysfunction in human disease occurs in a complex, three-dimensional space. However, many state-of-the-art techniques for studying cells rely on the dissociation of tissue to parse cells one by one, destroying any spatial context about the cellular environment. Using novel staining and imaging techniques, researchers can now probe the cellular constituents of human tissue with higher phenotypic resolution than previously possible and capture over 40 cell markers in a single biopsy, while preserving the spatial organization of cells in tissue. Artificial intelligence (AI) is necessary for high throughput processing of this data, as humans have difficulty interpreting information from multiple imaging channels. In her work, Madeleine Durkee develops and implements various AI methods for quantitative analysis of highly multiplexed microscopy images. Using AI, researchers can automatically find and characterize cells in high-content image data and extract spatial features describing the cellular organization of disease states. These spatial features might also be linked with clinical features such as therapy response or patient prognosis. Durkee is particularly interested in exploring the spatial context of immunity in various pathogenic states, ranging from autoimmunity to cancer.


Madeleine Durkee is a postdoctoral researcher in the Department of Radiology at the University of Chicago. She received her bachelor’s degree in Biomedical Engineering from Vanderbilt University in 2013 and her PhD in Biomedical Engineering from Texas A&M University in 2018. Her doctoral thesis focused on radiative transport modeling to help inform the design of optical imaging and sensing systems to detect disease in vivo. As a postdoc, she works with AI to quantify high-content optical microscopy images of human tissue samples. She is also interested in using AI to merge data from multiple imaging modalities to improve predictive models. In her free time, she likes to get away from the computer and run, hike, or walk; anything to stay active!

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