PhD, New York University
Out of Distribution Generalization in Machine Learning.
Machine Learning suffers from a fundamental problem: when systems are deployed in situations different than they were trained on, they can fail catastrophically. I aim to improve a large family of these problems by leveraging ideas from causality, deep learning, and the recognition of invariant statistical patterns. I am particularly focused in high-dimensional nonlinear problems (such as vision, language, and speech).