Soledad Villar is an Assistant Professor in Applied Mathematics and Statistics at Johns Hopkins University. Prior to that she worked with Joan Bruna and Afonso Bandeira at New York University and was affiliated with the Simons Foundation in New York City and UC Berkeley. She received her PhD from UT Austin supervised by Rachel Ward. Her research interests include equivariant machine learning, graph neural networks and mathematical foundations of deep learning. She is also interest in applications to computational biology.