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Soledad Villar is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University. Her research broadly focuses on computational methods for extracting information from data, with a particular interest in representation learning, graph neural networks, equivariant machine learning, data science, and optimization. She combines mathematical rigor with practical applications, aiming to develop innovative approaches that enhance our understanding of complex data structures. Throughout her academic career, Villar has contributed to advancing the field of mathematical data science through her rigorous exploration of state-of-the-art computational techniques. She continues to pursue her research with a commitment to fostering collaborations that bridge theoretical advancements with real-world challenges, thus making impactful contributions to her field.
Johns Hopkins University • Baltimore, MD
Teaching and researching in the field of Applied Mathematics and Statistics.
Department of Pathology - PhD in Pathobiology. GRE is not required.