About me
I am Nikola Jovanović, a PhD student at the Department of Computer Science, ETH Zürich. I am part of the Secure, Reliable, and Intelligent Systems Lab since January 2022. I previously obtained my Computer Science MSc at ETH Zürich in 2021, and my Computer Science BSc at Faculty of Computing, Union University, Belgrade in 2019.
I am currently on a leave, doing my internship at Meta FAIR in Paris.
Publications
2025
BaxBench: Can LLMs Generate Secure and Correct Backends?
Mark Vero, Niels Mündler, Victor Chibotaru, Veselin Raychev, Maximilian Baader, Nikola Jovanović, Jingxuan He, Martin Vechev
ICML
2025
Spotlight
Spotlight
Discovering Spoofing Attempts on Language Model Watermarks
Thibaud Gloaguen, Nikola Jovanović, Robin Staab, Martin Vechev
ICML
2025
MathConstruct: Challenging LLM Reasoning with Constructive Proofs
Mislav Balunović*, Jasper Dekoninck*, Nikola Jovanović, Ivo Petrov, Martin Vechev
ICML
2025
* Equal contribution
Ward: Provable RAG Dataset Inference via LLM Watermarks
Nikola Jovanović, Robin Staab, Maximilian Baader, Martin Vechev
ICLR
2025
Towards Watermarking of Open-Source LLMs
Thibaud Gloaguen, Nikola Jovanović, Robin Staab, Martin Vechev
WMARK @ ICLR
2025
Black-Box Detection of Language Model Watermarks
Thibaud Gloaguen, Nikola Jovanović, Robin Staab, Martin Vechev
ICLR
2025
Proof or Bluff? Evaluating LLMs on 2025 USA Math Olympiad
Ivo Petrov, Jasper Dekoninck, Lyuben Baltadzhiev, Maria Drencheva, Kristian Minchev, Mislav Balunović, Nikola Jovanović, Martin Vechev
arXiv
2025
2024
COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act
Philipp Guldimann, Alexander Spiridonov, Robin Staab, Nikola Jovanović, Mark Vero, Velko Vechev, Anna Gueorguieva, Mislav Balunović, Nikola Konstantinov, Pavol Bielik, Petar Tsankov, Martin Vechev
arXiv
2024
Watermark Stealing in Large Language Models
Nikola Jovanović, Robin Staab, Martin Vechev
ICML
2024
R2-FM@ICLR24 Oral
R2-FM@ICLR24 Oral
From Principle to Practice: Vertical Data Minimization for Machine Learning
Robin Staab, Nikola Jovanović, Mislav Balunović, Martin Vechev
IEEE S&P
2024
Back to the Drawing Board for Fair Representation Learning
Angéline Pouget, Nikola Jovanović, Mark Vero, Robin Staab, Martin Vechev
arXiv
2024
Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning
Kostadin Garov, Dimitar I. Dimitrov, Nikola Jovanović, Martin Vechev
ICLR
2024
2023
FARE: Provably Fair Representation Learning with Practical Certificates
Nikola Jovanović, Mislav Balunović, Dimitar I. Dimitrov, Martin Vechev
ICML
2023
2022
LAMP: Extracting Text from Gradients with Language Model Priors
Mislav Balunović*, Dimitar I. Dimitrov*, Nikola Jovanović, Martin Vechev
NeurIPS
2022
* Equal contribution
Private and Reliable Neural Network Inference
Nikola Jovanović, Marc Fischer, Samuel Steffen, Martin Vechev
ACM CCS
2022
On the Paradox of Certified Training
Nikola Jovanović*, Mislav Balunović*, Maximilian Baader, Martin Vechev
TMLR
2022
* Equal contribution
Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound
Claudio Ferrari, Mark Niklas Müller, Nikola Jovanović, Martin Vechev
ICLR
2022
2021
Towards Robust Graph Contrastive Learning
Nikola Jovanović, Zhao Meng, Lukas Faber, Roger Wattenhofer
The Workshop on Self-Supervised Learning for the Web -- WWW
2021
2018
Towards Sparse Hierarchical Graph Classifiers
Cătălina Cangea*, Petar Veličković*, Nikola Jovanović, Thomas Kipf, Pietro Liò
Relational Representation Learning Workshop -- NeurIPS
2018
* Equal contribution