Theresa Stadler
Postdoctoral Researcher & Lecturer at EPFL.
I am a Postdoctoral Researcher & Lecturer at the Security & Privacy Engineering Lab at EPFL (Switzerland) led by Carmela Troncoso.
My research focuses on the potentially harmful aspects of data processing systems centred around questions such as: How to evaluate the privacy properties of opaque data processing systems? What are the privacy limits of machine learning-based applications? What learning tasks are solvable under good privacy and good utility simultaneously? My work has been featured on multiple national media outlets and continues to inform policy makers on a national and European level.
Before joining EPFL, I previously worked as a researcher for Privitar, a London-based start-up, where I developed enterprise software that implements privacy-enhancing technologies and aims to makes these technologies available to organisations at scale. I hold a PhD in Computer Science from EPFL (Switzerland) and a Master’s degree in Computational Neuroscience (Biomathematics) from the University of Tübingen (Germany).
news
Jun 02, 2025 | Interview with Svea Eckert and Eva Wolfange on the “They talk Tech” podcast and for heise online about privacy engineering. |
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May 19, 2025 | Invited talk at the Responsible AI Seminar at Nokia Bell Labs. |
Nov 01, 2024 | Public defence of my PhD thesis On the Fundamental Limits of Privacy Enhancing Technologies |
Sep 10, 2024 | Talk and panel discussion at the Synthetic Data for AI Conference organised by the European Commission. |
Aug 25, 2024 | Lecture at the Brussels Privacy Hub Global Summer Academy for Privacy Law 2024 on Privacy-preserving Data Sharing in the Context of the European Data Strategy |
latest posts
Nov 01, 2024 | On the Fundamental Limits of Privacy-Enhancing Technologies |
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Aug 25, 2024 | Privacy-preserving Data Sharing and the European Data Strategy |
May 24, 2023 | Rethinking Data |
selected publications
- ConferenceThe Fundamental Limits of Least-Privilege LearningIn Proceedings of the 41th International Conference on Machine Learning (ICML 24), 2024
- ConferenceSynthetic Data – Anonymisation Groundhog DayIn 31st USENIX Security Symposium (USENIX Security 22), 2022
- PreprintDecentralized privacy-preserving proximity tracingarXiv preprint arXiv:2005.12273, 2020