About Me

I am a PhD candidate at IST Austria at the research group of Tom Henzinger. My research focuses on Machine Learning, Formal Methods, and Robotics.

Publications

* denotes equal contributions

2022

	Stability Verification in Stochastic Control Systems via Neural Network Supermartingales Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee, Thomas A. Henzinger Accepted at the AAAI Conference on Artificial Intelligence (AAAI) , 2022
	GoTube: Scalable Stochastic Verification of Continuous-Depth Models Sophie Gruenbacher, Mathias Lechner, Ramin Hasani, Daniela Rus, Thomas A. Henzinger, Scott A. Smolka, Radu Grosu Accepted at the AAAI Conference on Artificial Intelligence (AAAI) , 2022

2021

	Infinite Time Horizon Safety of Bayesian Neural Networks Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee, Thomas A. Henzinger In Conference on Neural Information Processing Systems (NeurIPS), 2021
	Causal Navigation by Continuous-time Neural Networks Charles J Vorbach, Ramin Hasani, Alexander Amini, Mathias Lechner, Daniela Rus In Conference on Neural Information Processing Systems (NeurIPS), 2021
	On-Off Center-Surround Receptive Fields for Accurate and Robust Image Zahra Babaiee, Ramin Hasani, Mathias Lechner, Daniela Rus, Radu Grosu In International Conference on Machine Learning (ICML), 2021
	Adversarial Training is Not Ready for Robot Learning Mathias Lechner, Ramin Hasani, Radu Grosu, Daniela Rus, Thomas A. Henzinger In IEEE International Conference on Robotics and Automation (ICRA), 2021
	Scalable Verification of Quantized Neural Networks Thomas A. Henzinger, Mathias Lechner, and Djordje Zikelic* (alphabetical) In AAAI Conference on Artificial Intelligence (AAAI) , 2021
	Liquid Time-constant Networks Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu Grosu In AAAI Conference on Artificial Intelligence (AAAI) , 2021
	On the Verification of Neural ODEs with Stochastic Guarantees Sophie Grünbacher, Ramin Hasani, Mathias Lechner, Jacek Cyranka, Scott A. Smolka, and Radu Grosu In AAAI Conference on Artificial Intelligence (AAAI) , 2021

2020

	Neural circuit policies enabling auditable autonomy Mathias Lechner, Ramin Hasani, Alexander Amini, Thomas A. Henzinger, Daniela Rus, and Radu Grosu In *Nature Machine Intelligence*, 2020
	The Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu Grosu In International Conference on Machine Learning (ICML), 2020
	Learning Representations for binary classification without backpropagation Mathias Lechner In International Conference on Learning Representations (ICLR), 2020
	An SMT Theory of Fixed-Point Arithmetic Marek Baranowski, Shaobo He, Mathias Lechner, Thanh Son Nguyen, and Zvonimir Rakamaric In International Joint Conference on Automated Reasoning (IJCAR), 2020
	Gershgorin Loss Stabilizes the Recurrent Neural Network Compartment of an End-To-End Robot Learning Scheme Mathias Lechner, Ramin Hasani, Daniela Rus, and Radu Grosu In IEEE International Conference on Robotics and Automation (ICRA), 2020
	How Many Bits Does it Take to Quantize Your Neural Network? Scheme Mirco Giacobbe, Thomas A. Henzinger, and Mathias Lechner* (alphabetical) In International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS), 2020
	Lagrangian Reachtubes: The Next Generation Sophie Gruenbacher, Jacek Cyranka, Mathias Lechner, Md. Ariful Islam, Scott Smolka, and Radu Grosu TC-DES and TC-HS Outstanding Student Paper Prize In IEEE Conference on Decision and Control (CDC), 2020

2019

	Response characterization for auditing cell dynamics in long short-term memory networks Ramin Hasani, Alexander Amini, Mathias Lechner, Felix Naser, Radu Grosu, and Daniela Rus In International Joint Conference on Neural Networks (IJCNN), 2019
	Designing worm-inspired neural networks for interpretable robotic control Mathias Lechner, Ramin Hasani, Manuel Zimmer, Thomas A. Henzinger, and Radu Grosu In IEEE International Conference on Robotics and Automation (ICRA), 2019

Awards

Outstanding Reviewer Award 2021 at the IEEE International Conference on Robotics and Automation (ICRA)

Distinguished Young Alumnus-Award 2018 Faculty of Informatics at TU Wien

Competitions

First Place In F1TENTH - Autonomous Racing Grand Prix at IFAC 2020

	Infinite Time Horizon Safety of Bayesian Neural Networks Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee, Thomas A. Henzinger In Conference on Neural Information Processing Systems (NeurIPS), 2021
	Causal Navigation by Continuous-time Neural Networks Charles J Vorbach, Ramin Hasani, Alexander Amini, Mathias Lechner, Daniela Rus In Conference on Neural Information Processing Systems (NeurIPS), 2021
	On-Off Center-Surround Receptive Fields for Accurate and Robust Image Zahra Babaiee, Ramin Hasani, Mathias Lechner, Daniela Rus, Radu Grosu In International Conference on Machine Learning (ICML), 2021
	Adversarial Training is Not Ready for Robot Learning Mathias Lechner, Ramin Hasani, Radu Grosu, Daniela Rus, Thomas A. Henzinger In IEEE International Conference on Robotics and Automation (ICRA), 2021
	Scalable Verification of Quantized Neural Networks Thomas A. Henzinger, Mathias Lechner, and Djordje Zikelic* (alphabetical) In AAAI Conference on Artificial Intelligence (AAAI) , 2021
	Liquid Time-constant Networks Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu Grosu In AAAI Conference on Artificial Intelligence (AAAI) , 2021
	On the Verification of Neural ODEs with Stochastic Guarantees Sophie Grünbacher, Ramin Hasani, Mathias Lechner, Jacek Cyranka, Scott A. Smolka, and Radu Grosu In AAAI Conference on Artificial Intelligence (AAAI) , 2021

	Neural circuit policies enabling auditable autonomy Mathias Lechner, Ramin Hasani, Alexander Amini, Thomas A. Henzinger, Daniela Rus, and Radu Grosu In *Nature Machine Intelligence*, 2020
	The Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu Grosu In International Conference on Machine Learning (ICML), 2020
	Learning Representations for binary classification without backpropagation Mathias Lechner In International Conference on Learning Representations (ICLR), 2020
	An SMT Theory of Fixed-Point Arithmetic Marek Baranowski, Shaobo He, Mathias Lechner, Thanh Son Nguyen, and Zvonimir Rakamaric In International Joint Conference on Automated Reasoning (IJCAR), 2020
	Gershgorin Loss Stabilizes the Recurrent Neural Network Compartment of an End-To-End Robot Learning Scheme Mathias Lechner, Ramin Hasani, Daniela Rus, and Radu Grosu In IEEE International Conference on Robotics and Automation (ICRA), 2020
	How Many Bits Does it Take to Quantize Your Neural Network? Scheme Mirco Giacobbe, Thomas A. Henzinger, and Mathias Lechner* (alphabetical) In International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS), 2020
	Lagrangian Reachtubes: The Next Generation Sophie Gruenbacher, Jacek Cyranka, Mathias Lechner, Md. Ariful Islam, Scott Smolka, and Radu Grosu TC-DES and TC-HS Outstanding Student Paper Prize In IEEE Conference on Decision and Control (CDC), 2020