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
- University of Stuttgart, October 2014 - April 2018 B.Sc. in Aerospace Engineering
Best Master's degree 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
- M.Sc. - Simone Barbaro, Out of Distribution Detection via Calibrated Confidence
- M.Sc. - Claudio Ferrari, Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound [ICLR'2022]
- RCS - Miklós Z. Horváth, Boosting Randomized Smoothing with Variance Reduced Classifiers [ICLR'2022 (Spotlight)]
- RCS - Miklós Z. Horváth, Robust and Accurate -- Compositional Architectures for Randomized Smoothings [SRML@ICLR'2022]
- M.Sc. - Miklós Z. Horváth, (De-)Randomized Smoothing for Decision Stump Ensembles [NeurIPS'2022]
- M.Sc. - Franziska Eckert, Certified Training: Small Boxes are All You Need [ICLR'2023 (Spotlight)]
- M.Sc. - Mustafa Zeqiri, Efficient Robustness Verification of Neural Ordinary Differential Equations [ICLR'2023]
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