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Recently, deep learning-based methods achieved promising performance in a variety of applications areas to include biomedicine and education. However, training deep learning-based methods requires a large amount of annotated data, which is time-consuming and labor-intensive. This seminar describes two approaches to this problem. One developed for educational applications uses Large Language Models (LLM) to label teacher questions and the second use a new Label-efficient Contrastive learning-based (LECL) model to detect and classify various types of nuclei in 3D immunofluorescent images.

Don Brown is the Senior Associate Dean for Research and Quantitative Foundation Distinguished Professor in Data Science and the W.S. Calcott Professor in the Department of Systems and Information Engineering. Brown is also the Founding Director of the Data Science Institute and Co-Director of the Translational Health Institute of Virginia. Recently, he was appointed as a board member of the Artificial Intelligence Industry Innovation Coalition for Healthcare (AI3C). Brown is one of 12 executives on the board, representing forward-thinking organizations, AI leadership, and innovation.