About Me
I am a DDSA PhD Fellow in Machine Learning @ Copenhagen University in the Department of Computer Science, where I am advised by Amartya Sanyal and Rasmus Pagh. My research interest lie in theoretical foundations and guarantees of Machine Learning, particularly in relation to Privacy and Robustness.
Prior to my PhD, I completed my undergraduate education in Mathematics at ETH Zürich. After completing my Master's degree, I worked as a Data Scientist for 2 years at Revolut, developing Machine Learning Models for the Risk Department.
Academic Service & Teaching
Program Membership
P1 Program “Data Privacy in Machine Learning” Pioneer Centre for AI, Denmark.
Local Organizer
IEEE Conference on Secure and Trustworthy Machine Learning (SaTML 2025).
Reviewer
TPDP (2025), AISTATS (2024, 2025).
Teaching Assistant
Privacy in Machine Learning (2025, 2024, University of Copenhagen), Mathematics of Machine Learning (2022, ETH Zürich), Partial Differential Equations (2020, ETH Zürich).
Publications
-
Preprint, 2026
-
NeurIPS, 2025 · TPDP, 2025
An iterative algorithm for differentially private k-PCA with adaptive noise
Advances in Neural Information Processing Systems (NeurIPS) 2025; Theory and Practice of Differential Privacy (TPDP) 2025