My Research

My research goal is to build deep neural networks that we can trust. Specifically, my research includes the topics

Formal verification of deep learning systems
Interpretable and explainable decision-making in neural networks
Robust robot learning

List of Publications

* denotes equal contributions

2023

	Robust flight navigation out of distribution with liquid neural networks Makram Chahine, Ramin Hasani, Patrick Kao, Aaron Ray, Ryan Shubert, Mathias Lechner, Alexander Amini, Daniela Rus Science Robotics*, 2023
	Dataset Distillation with Convexified Implicit Gradients Noel Loo, Ramin Hasani, Mathias Lechner, Daniela Rus Accepted at the International Conference on Machine Learning (ICML), 2023
	On the Forward Invariance of Neural ODEs Wei Xiao, Tsun-Hsuan Wang, Ramin Hasani, Mathias Lechner, Yutong Ban, Chuang Gan, Daniela Rus Accepted at the International Conference on Machine Learning (ICML), 2023
	A Learner-Verifier Framework for Neural Network Controllers and Certificates of Stochastic Systems Krishnendu Chatterjee, Thomas A Henzinger, Mathias Lechner, Đorđe Žikelić International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS), 2023
	Infrastructure-based End-to-End Learning and Prevention of Driver Failure Noam Buckman, Shiva Sreeram, Mathias Lechner, Yutong Ban, Ramin Hasani, Sertac Karaman, Daniela Rus IEEE International Conference on Robotics and Automation (ICRA), 2023
	Liquid Structural State-Space Models Ramin Hasani, Mathias Lechner, Tsun-Hsuan Wang, Makram Chahine, Alexander Amini, Daniela Rus International Conference on Learning Representations (ICLR) , 2023
	Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning Mathias Lechner, Alexander Amini, Daniela Rus, Thomas A. Henzinger IEEE Robotics and Automation Letters (RA-L) , 2023
	Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee, Thomas A. Henzinger, Daniela Rus AAAI Conference on Artificial Intelligence (AAAI) , 2023
	Learning Control Policies for Stochastic Systems with Reach-avoid Guarantees Đorđe Žikelić, Mathias Lechner, Thomas A. Henzinger, Krishnendu Chatterjee AAAI Conference on Artificial Intelligence (AAAI) , 2023

2022

	Closed-form continuous-time neural networks Ramin Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Aaron Ray, Max Tschaikowski, Gerald Teschl, Daniela Rus Nature Machine Intelligence, 2022
	PyHopper-A Plug-and-Play Hyperparameter Optimization Engine Mathias Lechner, Ramin Hasani, Sophie Neubauer, Philipp Neubauer, Daniela Rus Has it Trained Yet? NeurIPS 2022 Workshop, 2022
	Are All Vision Models Created Equal? A Study of the Open-Loop to Closed-Loop Causality Gap Mathias Lechner, Ramin Hasani, Alexander Amini, Tsun-Hsuan Wang, Thomas A Henzinger, Daniela Rus NeurIPS 2022 Machine Learning for Autonomous Driving Workshop (ML4AD), 2022
	Mixed-Memory RNNs for Learning Long-term Dependencies in Irregularly-sampled Time Series Mathias Lechner, Ramin Hasani Memory in Artificial and Real Intelligence (MemARI) NeurIPS 2022 Workshop, 2022
	Infrastructure-based End-to-End Learning and Prevention of Driver Failure Noam Buckman, Shiva Sreeram, Mathias Lechner, Yutong Ban, Ramin Hasani, Sertac Karaman, Daniela Rus 5th Robot Learning Workshop at NeurIPS: Trustworthy Robotics, 2022
	Learning Verifiable Representations Mathias Lechner PhD thesis, 2022
	Learning Stabilizing Policies in Stochastic Control Systems Đorđe Žikelić, Mathias Lechner, Krishnendu Chatterjee, Thomas A. Henzinger Workshop on Socially Responsible Machine Learning (SRML) at ICLR 2022, 2022
	Stability Verification in Stochastic Control Systems via Neural Network Supermartingales Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee, Thomas A. Henzinger 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 AAAI Conference on Artificial Intelligence (AAAI) , 2022
	Model-based versus Model-free Deep Reinforcement Learning for Autonomous Racing Cars Axel Brunnbauer, Luigi Berducci, Andreas Brandstätter, Mathias Lechner, Ramin Hasani, Daniela Rus, Radu Grosu IEEE International Conference on Robotics and Automation (ICRA)*, 2022

2021

	Infinite Time Horizon Safety of Bayesian Neural Networks Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee, Thomas A. Henzinger 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 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 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 IEEE International Conference on Robotics and Automation (ICRA), 2021
	Scalable Verification of Quantized Neural Networks Thomas A. Henzinger, Mathias Lechner, and Djordje Zikelic* (alphabetical) AAAI Conference on Artificial Intelligence (AAAI) , 2021
	Liquid Time-constant Networks Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu Grosu 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 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 *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 International Conference on Machine Learning (ICML), 2020
	Learning Representations for binary classification without backpropagation Mathias Lechner 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 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 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) 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 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 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 IEEE International Conference on Robotics and Automation (ICRA), 2019

Awards

Hyperion Research 2022 HPC Innovation Excellence Award for Liquid Machine Learning

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

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

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

	Infinite Time Horizon Safety of Bayesian Neural Networks Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee, Thomas A. Henzinger 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 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 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 IEEE International Conference on Robotics and Automation (ICRA), 2021
	Scalable Verification of Quantized Neural Networks Thomas A. Henzinger, Mathias Lechner, and Djordje Zikelic* (alphabetical) AAAI Conference on Artificial Intelligence (AAAI) , 2021
	Liquid Time-constant Networks Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu Grosu 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 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 *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 International Conference on Machine Learning (ICML), 2020
	Learning Representations for binary classification without backpropagation Mathias Lechner 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 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 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) 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 IEEE Conference on Decision and Control (CDC), 2020