Mark Vero

PhD student

mark.vero@inf.ethz.ch
CAB H 66

About me

I am Mark Vero, a PhD student at the Department of Computer Science, ETH Zürich. I am part of the Secure, Reliable, and Intelligent Systems Lab, supervised by Martin Vechev, since July 2023.

Education

  • ETH Zurich, since July 2023
    PhD Candidate in Computer Science
  • ETH Zurich, 2020 - 2023
    Masters in Electrical Engineering and Information Technology
  • ETH Zurich, 2017 - 2020
    Bachelors in Electrical Engineering and Information Technology

Teaching

  • Analysis 1, Autumn 2019, Autumn 2022
  • Analysis 2, Spring 2020, Spring 2022
  • Analysis 3, Autumn 2020
  • Introduction to Electrical Engineering, Spring 2019
  • Engineering Mechanics, Autumn 2018

Publications

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

Website
Paper
Code
Practical Attacks against Black-box Code Completion Engines
Slobodan Jenko, Jingxuan He, Niels Mündler, Mark Vero, Martin Vechev
arXiv 2024

Paper
CuTS: Customizable Tabular Synthetic Data Generation
Mark Vero, Mislav Balunović, Martin Vechev
ICML 2024

Paper
Code
Instruction Tuning for Secure Code Generation
Jingxuan He*, Mark Vero*, Gabriela Krasnopolska, Martin Vechev
ICML 2024 * Equal contribution

Paper
Code
A Synthetic Dataset for Personal Attribute Inference
Hanna Yukhymenko, Robin Staab, Mark Vero, Martin Vechev
NeurIPS Datasets and Benchmarks 2024

Paper
Code
Beyond Memorization: Violating Privacy Via Inference with Large Language Models
Robin Staab, Mark Vero, Mislav Balunović, Martin Vechev
ICLR 2024 Spotlight, 2024 PPPM-Award

Website
Paper
Code
Back to the Drawing Board for Fair Representation Learning
Angéline Pouget, Nikola Jovanović, Mark Vero, Robin Staab, Martin Vechev
arXiv 2024

Paper
Exploiting LLM Quantization
Kazuki Egashira, Mark Vero, Robin Staab, Jingxuan He, Martin Vechev
NeurIPS 2024 NextGenAISafety@ICML24 Oral

Website
Paper
Code
Private Attribute Inference from Images with Vision-Language Models
Batuhan Tömekçe, Mark Vero, Robin Staab, Martin Vechev
NeurIPS 2024

Paper
Code
Large Language Models are Advanced Anonymizers
Robin Staab, Mark Vero, Mislav Balunović, Martin Vechev
arXiv 2024

Paper

2023

TabLeak: Tabular Data Leakage in Federated Learning
Mark Vero, Mislav Balunović, Dimitar I. Dimitrov, Martin Vechev
ICML 2023

Paper
Code