Equivariant machine learning, structured like classical physics

Soledad Villar is an Assistant Professor at the Department of Applied Mathematics & Statistics, and at the Mathematical Institute for Data Science, 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. She studies optimization for data science, machine learning, equivariant representation learning and graph neural networks. Soledad is originally from Uruguay.