Soledad Villar is an Assistant Professor of Applied Mathematics and Statistics at Johns Hopkins University. She received her PhD in mathematics in 2017 from UT Austin and was later a research fellow at the Simons Institute in UC Berkeley, and a Moore-Sloan Research Fellow at NYU. Her honors and awards include delivering a commencement speech at UT Austin representing her graduating PhD class in 2017, a Fulbright Fellowship, and she was named a Rising Stars in Computational and Data Sciences in 2019. Her research is in mathematical data science, including mathematical theory of deep learning, graph learning, and applications. Her work has been funded by NSF, The Simons Foundation, ONR, and EOARD.