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Morgane Austern is an Assistant Professor of Statistics at Harvard University, affiliated with the Center for Mathematical Sciences and Applications (CMSA). She graduated with a PhD in Statistics from Columbia University in 2019. Following her graduation, she worked at Microsoft Research New England as a postdoctoral researcher from 2019 to 2021, collaborating with Peter Orbanz and Arian Maleki on limit theorems for dependent structured data. In recognition of her contributions to the field, she was named a Kavli Fellow by the National Academy of Sciences in 2022. In 2023, she was invited to speak at the Mathematical Foundation of Machine Learning Symposium focusing on AI and mathematical reasoning. Austern received the prestigious CAREER Award from the National Science Foundation in 2025. Her research interests encompass various domains including the Stein method and Gaussian Universality in probability theory, as well as advancements in machine learning theory such as graph neural networks and deep learning theory.
Microsoft Research New England • Cambridge, MA
Conducted research on limit theorems for dependent structured data.
Administered by the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences (GSAS).