Wyman N433
Research Areas Representation learning Graph neural networks Equivariant machine learning Mathematical data science

Professor Soledad Villar is an assistant professor in the Department of Applied Mathematics and Statistics and at the Mathematical Institute for Data Science at Johns Hopkins University.  

She received her PhD in mathematics from University in Texas at Austin and was a research fellow at New York University as well as the Simons Institute in University of California Berkeley. Her mathematical interests are in computational methods for extracting information from data. In particular, she studies optimization for data science, machine learning, equivariant representation learning and graph neural networks.