About me

I am Mark Müller, 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, starting October 2020.

Education

  • ETH Zurich, September 2019 - October 2020
    Visiting Student in the Department of Computer Science
  • University of Stuttgart, October 2018 - October 2020
    M.Sc. in Aerospace Engineering
  • Best Master's degree in Aerospace Engineering
  • University of Stuttgart, October 2014 - April 2018
    B.Sc. in Aerospace Engineering
  • Best Bachelor's degree in Aerospace Engineering

Publications

2023

Efficient Certified Training and Robustness Verification of Neural ODEs
Mustafa Zeqiri, Mark Niklas Müller, Marc Fischer, Martin Vechev
ICLR 2023
Certified Training: Small Boxes are All You Need
Mark Niklas Müller*, Franziska Eckert*, Marc Fischer, Martin Vechev
ICLR 2023 * Equal contribution
First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)
Christopher Brix, Mark Niklas Müller, Stanley Bak, Changliu Liu, Taylor T. Johnson
STTT ExPLAIn 2023

2022

The Third International Verification of Neural Networks Competition (VNN-COMP 2022): Summary and Results
Mark Niklas Müller*, Christopher Brix*, Stanley Bak, Changliu Liu, Taylor T. Johnson
arXiv 2022 * Equal contribution
(De-)Randomized Smoothing for Decision Stump Ensembles
Miklós Z. Horváth*, Mark Niklas Müller*, Marc Fischer, Martin Vechev
NeurIPS 2022 * Equal contribution
Robust and Accurate - Compositional Architectures for Randomized Smoothing
Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin Vechev
SRML@ICLR 2022
Boosting Randomized Smoothing with Variance Reduced Classifiers
Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin Vechev
ICLR (Spotlight) 2022
Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound
Claudio Ferrari, Mark Niklas Müller, Nikola Jovanović, Martin Vechev
ICLR 2022
PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations
Mark Niklas Müller, Gleb Makarchuk, Gagandeep Singh, Markus Püschel, Martin Vechev
POPL 2022

2021

Effective Certification of Monotone Deep Equilibrium Models
Mark Niklas Müller, Robin Staab, Marc Fischer, Martin Vechev
arXiv 2021
Certify or Predict: Boosting Certified Robustness with Compositional Architectures
Mark Niklas Müller, Mislav Balunović, Martin Vechev
ICLR 2021

Supervised students

Work experience

  • Dr. Ing. h.c. F. Porsche AG, Weissach, DE, 11/2018 - 08/2019
    Working Student
  • Bosch Rexroth AG, Stuttgart, DE, 09/2018 - 10/2018
    Data Science Intern
  • Mercedes-AMG Petronas Formula One Team, Brackley, GB, 07/2017 - 07/2018
    Industrial Placement - Aerodynamicist

Awards

  • LRT Award for the best overall Master degree in Aerospace Engineering
  • AIRBUS Defence & Space Award for the best overall Bachelor degree in Aerospace Engineering