Tudor Cebere

I am a third-year PhD student at Inria 🇫🇷, fortunate to be supervised by Aurélien Bellet. I am a recipient of the Google PhD Fellowship 2025 in Privacy, Safety, and Security. Since August 2024, I am also an Associate in Computer Science at Harvard, working with the OpenDP group. During my PhD, I have also completed research visits at the Vector Institute 🇨🇦 (hosted by Nicolas Papernot) and Harvard 🇺🇸 (hosted by the OpenDP group).

I am interested in differential privacy (DP) and its applications in creating trustworthy machine learning models. In particular, I am interested in the guarantees offered by differential privacy in various threat models and the associated practical guarantees, beyond the worst-case ones.

I graduated from École normale supérieure de Lyon (ENS Lyon) with a masters in Theoretical CS, where I was fortunate to receive an Ampère Scholarship of Excellence. Before that, I received my BSc in EECS from Politehnica University of Bucharest 🇷🇴.

I am always open for new collaborations or for advising motivated students for their BSc or MSc thesis. If interested, drop me an email at tudor dot cebere@inria.fr

Publications

2025	Private Rate-Constrained Optimization with Applications to Fair Learning – Mohammad Yaghini, Tudor Cebere, Michael Menart, Aurélien Bellet, Nicolas Papernot – Arxiv – Preprint
2024	Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model – Tudor Cebere, Aurélien Bellet, Nicolas Papernot – Proceedings of the 13th International Conference on Learning Representations. – Conference
2024	Confidential-DPproof: Confidential Proof of Differentially Private Training – Ali Shahin Shamsabadi, Gefei Tan, Tudor Cebere, Aurélien Bellet, Hamed Haddadi, Nicolas Papernot, Xiao Wang, Adrian Weller – Proceedings of the 12th International Conference on Learning Representations. – Conference (+spotlight)
2021	Syft 0.5: A platform for universally deployable structured transparency – Adam James Hall, Madhava Jay, Tudor Cebere, Bogdan Cebere et al. – Distributed and Private Machine Learning Workshop at the 9th International Conference on Learning Representations. – Workshop
2021	Pyvertical: A vertical federated learning framework for multi-headed splitnn – Daniele Romanini, Adam James Hall, Pavlos Papadopoulos, Tom Titcombe, Abbas Ismail, Tudor Cebere, Robert Sandmann, Robin Roehm, Michael A Hoeh – Distributed and Private Machine Learning Workshop at the 9th International Conference on Learning Representations. – Workshop