TBA
Presenter: David Alvarez-Miles (Harvard University)
Title: TBA
Date/Time: TBA
Abstract: TBA
Biography: David Alvarez-Miles is an Assistant Professor of Computer Science at Harvard SEAS where he leads the Data-Centric Machine Learning (DCML) group. He is also an Associate Faculty at the Kempner Institute, and has affiliations with the Center for Research on Computation and Society and the Harvard Data Science Initiative. He is also a researcher at Microsoft Research New England.
David’s research seeks to make machine learning more broadly applicable (especially to data-poor applications) and trustworthy (e.g., robust and interpretable). He is particularly interested in the implications of these two directions for applications in the natural and medical sciences. David’s approach to the first of these goals draws on ideas from statistics, optimization, and applied mathematics, especially optimal transport, which he has used to develop methods to mitigate data scarcity by various types of geometric dataset manipulations: alignment, comparison, generation, and transformation. As for trustworthy machine learning, David has 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. In the past, David has worked on various aspects of learning with highly-structured data such as text or graphs, ranging from learning representations of structured objects, to generating them, to interpreting models that operate on them.