T. A. Henzinger, “Quantitative monitoring of software,” in Software Verification, New Haven, CT, United States, 2022, vol. 13124, pp. 3–6.

E. Bartocci, T. Ferrere, T. A. Henzinger, D. Nickovic, and A. O. Da Costa, “Information-flow interfaces,” in Fundamental Approaches to Software Engineering, Munich, Germany, 2022, vol. 13241, pp. 3–22.

T. A. Henzinger, N. A. Mazzocchi, and N. E. Sarac, “Abstract monitors for quantitative specifications,” in 22nd International Conference on Runtime Verification, Tbilisi, Georgia, 2022, vol. 13498, pp. 200–220.

R. Hasani et al., “Closed-form continuous-time neural networks,” Nature Machine Intelligence, vol. 4, no. 11. Springer Nature, pp. 992–1003, 2022.

M. Lechner, “Learning verifiable representations,” Institute of Science and Technology Austria, 2022.

K. Doveri, P. Ganty, and N. A. Mazzocchi, “FORQ-based language inclusion formal testing,” in Computer Aided Verification, Haifa, Israel, 2022, vol. 13372, pp. 109–129.

S. Bose, T. A. Henzinger, K. Lehtinen, S. Schewe, and P. Totzke, “History-deterministic timed automata are not determinizable,” in 16th International Conference on Reachability Problems, Kaiserslautern, Germany, 2022, vol. 13608, pp. 67–76.

S. A. Gruenbacher et al., “GoTube: Scalable statistical verification of continuous-depth models,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 6. Association for the Advancement of Artificial Intelligence, pp. 6755–6764, 2022.

M. Lechner, D. Zikelic, K. Chatterjee, and T. A. Henzinger, “Stability verification in stochastic control systems via neural network supermartingales,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 7. Association for the Advancement of Artificial Intelligence, pp. 7326–7336, 2022.

D. Zikelic, M. Lechner, K. Chatterjee, and T. A. Henzinger, “Learning stabilizing policies in stochastic control systems,” arXiv. .

D. Zikelic, M. Lechner, T. A. Henzinger, and K. Chatterjee, “Learning control policies for stochastic systems with reach-avoid guarantees,” arXiv. .

F. Mühlböck and R. Tate, “Transitioning from structural to nominal code with efficient gradual typing,” Proceedings of the ACM on Programming Languages, vol. 5. Association for Computing Machinery, 2021.

S. Grunbacher, R. Hasani, M. Lechner, J. Cyranka, S. A. Smolka, and R. Grosu, “On the verification of neural ODEs with stochastic guarantees,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 13, pp. 11525–11535.

R. Hasani, M. Lechner, A. Amini, D. Rus, and R. Grosu, “Liquid time-constant networks,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 9, pp. 7657–7666.

Z. Babaiee, R. Hasani, M. Lechner, D. Rus, and R. Grosu, “On-off center-surround receptive fields for accurate and robust image classification,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 478–489.

C. J. Vorbach, R. Hasani, A. Amini, M. Lechner, and D. Rus, “Causal navigation by continuous-time neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021.

B. Kragl and S. Qadeer, “The Civl verifier,” in Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design, Virtual, 2021, vol. 2, pp. 143–152.

G. Dubach and F. Mühlböck, “Formal verification of Zagier’s one-sentence proof,” arXiv. .

T. A. Henzinger, M. Lechner, and D. Zikelic, “Scalable verification of quantized neural networks,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 5A, pp. 3787–3795.

M. Lechner, Ð. dikeli, K. Chatterjee, and T. A. Henzinger, “Infinite time horizon safety of Bayesian neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021.