Morgane Austern

I am an assistant professor of Statistics at Harvard University and an affiliate of the CMSA.   I graduated with a PhD in statistics from Columbia University in 2019 where I worked in collaboration with Peter Orbanz and Arian Maleki on limit theorems for dependent and structured data.  For two great years (2019-2021), I was a  postdoctoral researcher at Microsoft Research New England. In 2022 I was named a Kavli fellow by the National Academy of science. In 2023 I was invited to speak at the National Academies of Science, Engineering and Medecine on the mathematical foundation of machine learning in a symposium on AI for mathematical reasoning. In 2025 I received a CAREER Award from the NSF. 

My research interests include: 

• Probability theory: Stein method, Gaussian Universality, Concentration inequalities, Limit theorems for dependent and structured data, Free probability, Ergodic theory, Optimal transport etc..

• Machine learning theory: Graph neural networks, Data augmentation, Deep learning theory, Graph embeddings etc...

•  Statistical inference: Resampling methods (bootstrap, cross-validation), Causal inference, High-Dimensional statistics for dependent data, Network theory, etc.

My research is supported by ONR and NSF funding