LB4TL: Smooth Semantics for Temporal Logic for Scalable Training of Neural Feedback Controllers.
Hashemi, Navid, et al. "Scaling Learning based Policy Optimization for Temporal Tasks via Dropout." arXiv preprint arXiv:2403.15826 (2024).
Hashemi, Navid, et al. "Scaling Learning based Policy Optimization for Temporal Tasks via Dropout." arXiv preprint arXiv:2403.15826 (2024).
Hashemi, Navid, et al. "Scaling Learning based Policy Optimization for Temporal Tasks via Dropout." arXiv preprint arXiv:2403.15826 (2024).
Hashemi, Navid, Justin Ruths, and Jyotirmoy V. Deshmukh. "Convex Optimization-based Policy Adaptation to Compensate for Distributional Shifts." 2023 62nd IEEE Conference on Decision and Control (CDC). IEEE, 2023.
Hashemi, Navid, et al. "Data-Driven Reachability Analysis of Stochastic Dynamical Systems with Conformal Inference." 2023 62nd IEEE Conference on Decision and Control (CDC). IEEE, 2023.
Qin, X., Hashemi, N., Lindemann, L., & Deshmukh, J. V. (2023, October). Conformance testing for stochastic cyber-physical systems. In CONFERENCE ON FORMAL METHODS IN COMPUTER-AIDED DESIGN–FMCAD 2023 (p. 294).
Hashemi, Navid, et al. "A neurosymbolic approach to the verification of temporal logic properties of learning-enabled control systems." Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023). 2023.
Hashemi, Navid, et al. "Risk-awareness in learning neural controllers for temporal logic objectives." 2023 American Control Conference (ACC). IEEE, 2023.
Hashemi, Navid, and Justin Ruths. Co-design for resilience and performance. IEEE Transactions on Control of Network Systems (2022).
Umsonst, D., Hashemi, N., Sandberg, H., & Ruths, J. (2022, August). Practical detectors to identify worst-case attacks. In 2022 IEEE Conference on Control Technology and Applications (CCTA) (pp. 197-204). IEEE.
Hashemi, Navid, Justin Ruths, and Mahyar Fazlyab. "Certifying incremental quadratic constraints for neural networks via convex optimization." Learning for Dynamics and Control. PMLR, 2021.
Hashemi, Navid, Mahyar Fazlyab, and Justin Ruths. "Performance bounds for neural network estimators: Applications in fault detection." 2021 American Control Conference (ACC). IEEE, 2021.
Renganathan, V., Hashemi, N., Ruths, J., & Summers, T. H. (2021). Higher-order moment-based anomaly detection. IEEE Control Systems Letters, 6, 211-216.
Renganathan, V., Hashemi, N., Ruths, J., & Summers, T. H. (2020, July). Distributionally robust tuning of anomaly detectors in cyber-physical systems with stealthy attacks. In 2020 American Control Conference (ACC) (pp. 1247-1252). IEEE.
Hashemi, Navid, and Justin Ruths. "Gain design via LMIs to minimize the impact of stealthy attacks." 2020 American Control Conference (ACC). IEEE, 2020.
Hashemi, Navid, et al. "Filtering approaches for dealing with noise in anomaly detection." 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019.
Hashemi, Navid, and Justin Ruths. "Generalized chi-squared detector for lti systems with non-gaussian noise." 2019 American Control Conference (ACC). IEEE, 2019.
Hashemi, Navid, Carlos Murguia, and Justin Ruths. "A comparison of stealthy sensor attacks on control systems." 2018 Annual American Control Conference (ACC). IEEE, 2018.
Talk at The 8th IFAC Conference on Analysis and Design of Hybrid Systems, Boulder, Colorado
Talk at 43rd Southern California Control Workshop, Los Angeles, California
Conference Proceedings Talk at 14TH ACM/IEEE INTERNATIONAL CONFERENCE ON CYBER-PHYSICAL SYSTEMS, San Antonio Texas, USA
Conference proceedings talk at Annual American Control Conference ACC 2023, San Diego, CA
Poster Presentation Talk at Texas Systems Day 2019, College Station, Texas, USA
Conference proceedings talk at Annual American Control Conference ACC 2018, Milwaukee, Winsconsin, USA
Poster Presentation Talk at Texas Systems Day 2018, Dallas, Texas, USA