Adela DePavia
Adela DePavia
PhD Candidate in Computational & Applied Math
The University of Chicago
PhD Candidate in Computational & Applied Math
The University of Chicago
I have broad research interests in optimization and theoretical foundations of machine learning. One of the core themes of my research is leveraging structure in optimization. Some concrete examples include:
Linear subspace structure for adaptive optimization algorithms
Hypergraph structure for capturing higher-order interactions in datasets
Graph structure for learning-based search algorithms
For a more detailed overview of my research, please see the Publications tab.