I am a postdoctoral researcher at Stanford University. I previously earned my PhD at EPFL after graduating from Ecole Polytechnique. I also interned at Amazon.
My research develops foundations and methods for making machine learning systems trustworthy when data are private, participants are unreliable, or some data must later be forgotten. I study privacy, robustness, and machine unlearning, including in distributed learning systems.
Email: hauollay [at] drofnats [dot] edu
Privacy versus Robustness: Limits and Algorithms in Federated Learning [talk] Simons Institute for the Theory of Computing, February 24, 2026.
Trustworthy Machine Learning [video] UM6P College of Computing, 2024.
Trustworthy Machine Learning: Robustness and Privacy [video] MoroccoAI Seminar, 2023.
Robust Sparse Voting [video] PODC 2022; Tournesol Talks, 2023.