Soledad Villar

Soledad is an Assistant Professor of Applied Mathematics and Statistics at Johns Hopkins University. She is Uruguayan and did her undergraduate studies in Uruguay (Mathematics and Informatics Engineering) and got a masters’ degree in Mathematics from UDELAR. She received her PhD in Mathematics from UT Austin and held postdoctoral fellowships in UC Berkeley and New York University. Her research interest is in the intersection of applied mathematics and machine learning. In particular mathematical theory of deep learning, graph neural networks, and equivariant machine learning.