Soledad Villar is a Moore-Sloan Research Fellow at the New York University Center for Data Science. Villar received her Ph.D. in mathematics from the University of Texas at Austin in 2017, supervised by Rachel Ward. Prior to her Ph.D., Villar completed a master’s degree in number theory under the supervision of Gonzalo Tornaria and a bachelor’s degree in mathematics and informatics engineering in her home country, Uruguay.
Villar is interested in optimization, statistics, machine learning and applied harmonic analysis. She is also interested in data-related problems from geometric, topologic and algorithmic points of view. Her current projects lie in the interface of statistical physics and optimization.