My approach to the first of these goals draws on ideas from statistics, optimization, and applied mathemtics, especially optimal transport, which I have used to develop methods to mitigate data scarcity by various types of geometric dataset manipulations: alignment, comparison, generation, and transformation. This talk provides a high-level overview of this part of my work.

As for trustworthy machine learning, I have worked on methods for explaining predictions of black box models, showed their lack of robustness, proposed methods to robustify them, and sought inspiration in the social sciences to make them human-centered.