For a more detailed overview of her research, please see the Publications tab.
Research Experience
Research areas include linear subspace structure for adaptive optimization algorithms, hypergraph structure for capturing higher-order interactions in datasets, and graph structure for learning-based search algorithms.
Education
PhD Candidate in Computational & Applied Math at the University of Chicago, advised by Professors Lorenzo Orecchia and Rebecca Willett.
Background
Broad research interests in optimization and theoretical foundations of machine learning. A core theme of her research is leveraging structure in optimization.
Miscellany
Office: 307 George Herbert Jones Lab
Email: (first initial)depavia (at) uchicago (dot) edu