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I will be graduating in early 2022, and on the market for jobs. I'm open to research opportunities in either academia or industry, and happy to discuss freelancing and any potential big ideas you might have.

Research Interests

Programming Language Design and Analysis, Machine Learning, AI Security, AI Safety, AI Trustworthiness, Program Synthesis, Computational Geometry, Formal Logic, ML Foundations.



The Fundamental Limits of Neural Networks for Interval Certified Robustness
Matthew Mirman, Maximilian Baader, Martin Vechev
TMLR 2022


Robustness Certification with Generative Models
Matthew Mirman, Alexander Hägele, Timon Gehr, Pavol Bielik, Martin Vechev
PLDI 2021


Universal Approximation with Certified Networks
Maximilian Baader, Matthew Mirman, Martin Vechev
ICLR 2020
Robustness Certification of Generative Models
Mathew Mirman, Timon Gehr, Martin Vechev
arXiv 2020


Online Robustness Training for Deep Reinforcement Learning
Marc Fischer, Matthew Mirman, Steven Stalder, Martin Vechev
arXiv 2019
A Provable Defense for Deep Residual Networks
Matthew Mirman, Gagandeep Singh, Martin Vechev
ArXiv 2019


Fast and Effective Robustness Certification
Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin Vechev
NIPS 2018
Training Neural Machines with Trace-Based Supervision
Matthew Mirman, Dimitar Dimitrov, Pavle Djordjevich, Timon Gehr, Martin Vechev
ICML 2018
AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation
Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, Martin Vechev
IEEE S&P 2018


  • ETH Zurich, January 2017
    PhD Candidate in Computer Science
  • CMU - SCS , August 2012 – May 2014
    MSCS in Computer Science
  • CMU - SCS , August 2009 – May 2012
    BS in Computer Science