PeopleofSuperintelligence

Researcher

Morgane Austern

Morgane Austern is a statistician who works on machine-learning theory, probability, and structured data. She earned a statistics PhD at Columbia in 2019 and worked as a postdoctoral researcher at Microsoft Research New England from 2019 to 2021. Her work includes theory for network embeddings, research on data augmentation and high-dimensional risk, and a National Academies discussion of uncertainty and risk in machine learning. She received an NSF CAREER Award in 2025 and became a Sloan Fellow in 2026; as of October 2026, she is an assistant professor of statistics at Harvard.

Works
34
Citations
151
h-index
5
Badges

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

  1. 2019

    A PhD in statistics

    She completes a PhD in statistics at Columbia University in 2019, working with Peter Orbanz and Arian Maleki on limit theorems for dependent and structured data.

  2. 2019–2021

    Postdoctoral research at Microsoft

    From 2019 to 2021, she works as a postdoctoral researcher at Microsoft Research New England.

  3. 2023

    Network embeddings and ML theory

    With Andrew Davison, she publishes a 2023 theory of network embeddings that brings subsampling methods such as node2vec into a single framework and characterizes the learned vectors' asymptotic distribution. A National Academies workshop report published in 2023 describes her discussion of uncertainty quantification and risk estimation for classification.

  4. 2025

    An NSF CAREER Award

    In 2025, she receives an NSF CAREER Award.

  5. 2025

    Data augmentation under dependence

    With Matthew Esmaili Mallory and Kevin Han Huang, she coauthors a 2025 study of data augmentation's effect on the asymptotic risk of high-dimensional logistic regression.

  6. 2026

    A Sloan fellowship

    She is named a Sloan Fellow in 2026.

  7. As of October 2026

    Assistant professor at Harvard

    As of October 2026, she is an assistant professor of Statistics at Harvard University. Her research group focuses on high-dimensional statistics, probability theory, machine-learning theory, and graph data.

Career

  1. NowHarvard UniversityAssistant Professor of Statistics
  2. 2019–2021Microsoft ResearchPostdoctoral Researcher

Education

  1. 2019Columbia UniversityPhD in statistics

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Morgane Austern sites.google.com. Checked 27 October 2026.
  2. Asymptotics of Network Embeddings Learned via Subsampling jmlr.org. Checked 27 October 2026.
  3. Artificial Intelligence to Assist Mathematical Reasoning: Proceedings of a Workshop nationalacademies.org. Checked 27 October 2026.
  4. Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation arxiv.org. Checked 27 October 2026.
  5. Postdoctoral Fellow in Statistics academicpositions.harvard.edu. Checked 27 October 2026.