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Soledad Villar is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University. She is also affiliated with the Mathematical Institute for Data Science. Soledad is a member of the Data Science AI Institute and received her PhD in Mathematics from the University of Texas at Austin. She has held a research fellowship at New York University and the Simons Institute at the University of California, Berkeley. Her research focuses on areas such as optimization, data science, machine learning, equivariant representation learning, and graph neural networks. Her work emphasizes the use of computational methods to extract information from data, particularly in the context of advanced machine learning models.
Department of Pathology - PhD in Pathobiology. GRE is not required.