Tudor Cebere

I am an third-year PhD student at Inria 🇫🇷, supervised by Aurélien Bellet. During my PhD, I visited 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 🇷🇴.

Publications

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
	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
	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