Research outputs
2025
AutoTestForge: A Multidimensional Automated Testing Framework for Natural Language Processing Models
Xing, H., Tian, C., Zhao, L., Ma, Z., Wang, W., Zhang, N., . . . Duan, Z. (2025). AutoTestForge: A Multidimensional Automated Testing Framework for Natural Language Processing Models. ACM Transactions on Software Engineering and Methodology. doi:10.1145/3783995
Efficient and assured reinforcement learning-based building HVAC control with heterogeneous expert-guided training.
Xu, S., Fu, Y., Wang, Y., Yang, Z., Huang, C., O'Neill, Z., . . . Zhu, Q. (2025). Efficient and assured reinforcement learning-based building HVAC control with heterogeneous expert-guided training.. Scientific reports, 15(1), 7677. doi:10.1038/s41598-025-91326-z
Bridging Dimensions: Confident Reachability for High-Dimensional Controllers
Geng, Y., Baldauf, J. B., Dutta, S., Huang, C., & Ruchkin, I. (2025). Bridging Dimensions: Confident Reachability for High-Dimensional Controllers. In Lecture Notes in Computer Science (pp. 381-402). Springer Nature Switzerland. doi:10.1007/978-3-031-71162-6_20
Case Study: Runtime Safety Verification of Neural Network Controlled System
Yang, F., Zhan, S. S., Wang, Y., Huang, C., & Zhu, Q. (2025). Case Study: Runtime Safety Verification of Neural Network Controlled System. In Unknown Conference (pp. 205-217). Springer Nature Switzerland. doi:10.1007/978-3-031-74234-7_13
Directly Forecasting Belief for Reinforcement Learning with Delays
Wu, Q., Wang, Y., Zhan, S. S., Wang, Y., Lin, C. W., Lv, C., . . . Huang, C. (2025). Directly Forecasting Belief for Reinforcement Learning with Delays. In Proceedings of Machine Learning Research Vol. 267 (pp. 67810-67832).
Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning
Wang, Y., Wu, Q., Ashley, D. R., Faccio, F., Li, W., Huang, C., & Schmidhuber, J. (2025). Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning. In Proceedings of Machine Learning Research Vol. 267 (pp. 64914-64936).
2024
SaliencyCut: Augmenting plausible anomalies for anomaly detection
Ye, J., Hu, Y., Yang, X., Wang, Q. -F., Huang, C., & Huang, K. (2024). SaliencyCut: Augmenting plausible anomalies for anomaly detection. Pattern Recognition, 110508. doi:10.1016/j.patcog.2024.110508
REGLO: Provable Neural Network Repair for Global Robustness Properties
Fu, F., Wang, Z., Zhou, W., Wang, Y., Fan, J., Huang, C., . . . Li, W. (2024). REGLO: Provable Neural Network Repair for Global Robustness Properties. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 12061-12071). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v38i11.29094
Analytically Determining the Robustness of Binarized Neural Networks
Alzahrani, S. M., Schewe, S., Huang, C., & Huang, X. (2024). Analytically Determining the Robustness of Binarized Neural Networks. In 2024 International Conference on Machine Learning and Applications (ICMLA) (pp. 597-604). IEEE. doi:10.1109/icmla61862.2024.00087
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
Wu, Q., Zhan, S. S., Wang, Y., Wang, Y., Lin, C. W., Lv, C., . . . Huang, C. (2024). Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays. In Proceedings of Machine Learning Research Vol. 235 (pp. 53973-53998).
Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling
Jiao, R., Wang, Y., Liu, X., Zhan, S. S., Huang, C., & Zhu, Q. (2024). Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 565-572). IEEE. doi:10.1109/iros58592.2024.10802438
RealDriftGenerator: A Novel Approach to Generate Concept Drift in Real World Scenario
Lin, B., Huang, C., Zhu, X., & Jin, N. (2024). RealDriftGenerator: A Novel Approach to Generate Concept Drift in Real World Scenario. In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 1124-1129). IEEE. doi:10.1109/smc54092.2024.10831569
State-Wise Safe Reinforcement Learning with Pixel Observations
Zhan, S. S., Wang, Y., Wu, Q., Jiao, R., Huang, C., & Zhu, Q. (2024). State-Wise Safe Reinforcement Learning with Pixel Observations. In Proceedings of Machine Learning Research Vol. 242 (pp. 1187-1201).
Variational Delayed Policy Optimization
Wu, Q., Zhan, S. S., Wang, Y., Wang, Y., Lin, C. W., Lv, C., . . . Huang, C. (2024). Variational Delayed Policy Optimization. In Advances in Neural Information Processing Systems Vol. 37.
2023
POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems
Wang, Y., Zhou, W., Fan, J., Wang, Z., Li, J., Chen, X., . . . Zhu, Q. (2023). POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 1. doi:10.1109/tcad.2023.3331215
System Verification and Runtime Monitoring with Multiple Weakly-Hard Constraints
Hsieh, Y. -T., Chang, T. -T., Tsai, C. -J., Wu, S. -L., Bai, C. -Y., Chang, K. -C., . . . Zhu, Q. (2023). System Verification and Runtime Monitoring with Multiple Weakly-Hard Constraints. ACM TRANSACTIONS ON CYBER-PHYSICAL SYSTEMS, 7(3). doi:10.1145/3603380
A Safety-Guaranteed Framework for Neural-Network-Based Planners in Connected Vehicles under Communication Disturbance
Chang, K. K. -C., Liu, X., Lin, C. -W., Huang, C., & Zhu, Q. (2023). A Safety-Guaranteed Framework for Neural-Network-Based Planners in Connected Vehicles under Communication Disturbance. In 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE. doi:10.23919/date56975.2023.10137184
Joint Differentiable Optimization and Verification for Certified Reinforcement Learning
Wang, Y., Zhan, S., Wang, Z., Huang, C., Wang, Z., Yang, Z., & Zhu, Q. (2023). Joint Differentiable Optimization and Verification for Certified Reinforcement Learning. In Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023) (pp. 132-141). ACM. doi:10.1145/3576841.3585919
Mixed-Traffic Intersection Management Utilizing Connected and Autonomous Vehicles as Traffic Regulators
Chen, P. -C., Liu, X., Lin, C. -W., Huang, C., & Zhu, Q. (2023). Mixed-Traffic Intersection Management Utilizing Connected and Autonomous Vehicles as Traffic Regulators. In 2023 28TH ASIA AND SOUTH PACIFIC DESIGN AUTOMATION CONFERENCE, ASP-DAC (pp. 52-57). doi:10.1145/3566097.3567849
Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments
Wang, Y., Zhan, S. S., Jiao, R., Wang, Z., Jin, W., Yang, Z., . . . Zhu, Q. (2023). Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments. In Proceedings of Machine Learning Research Vol. 202 (pp. 36593-36604).
Safety-Assured Design and Adaptation of Connected and Autonomous Vehicles
Chen, X., Fan, J., Huang, C., Jiao, R., Li, W., Liu, X., . . . Zhu, Q. (2023). Safety-Assured Design and Adaptation of Connected and Autonomous Vehicles. In Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems (pp. 735-757). Springer International Publishing. doi:10.1007/978-3-031-28016-0_26
Verification and Design of Robust and Safe Neural Network-enabled Autonomous Systems
Zhu, Q., Li, W., Huang, C., Chen, X., Zhou, W., Wang, Y., . . . Fu, F. (2023). Verification and Design of Robust and Safe Neural Network-enabled Autonomous Systems. In 2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton) (pp. 1-8). IEEE. doi:10.1109/allerton58177.2023.10313451
2022
ARCH-COMP22 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants
Manzanas Lopez, D., Althoff, M., Benet, L., Chen, X., Fan, J., Forets, M., . . . Zhu, Q. (2022). ARCH-COMP22 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants. In EPiC Series in Computing. EasyChair. doi:10.29007/wfgr
POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems
Huang, C., Fan, J., Chen, X., Li, W., & Zhu, Q. (2022). POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems. In Unknown Conference (pp. 414-430). Springer International Publishing. doi:10.1007/978-3-031-19992-9_27
Design-while-Verify: Correct-by-Construction Control Learning with Verification in the Loop
Wang, Y., Huang, C., Wang, Z., Wang, Z., & Zhu, Q. (2022). Design-while-Verify: Correct-by-Construction Control Learning with Verification in the Loop. In PROCEEDINGS OF THE 59TH ACM/IEEE DESIGN AUTOMATION CONFERENCE, DAC 2022 (pp. 925-930). doi:10.1145/3489517.3530556
Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding (Extended Abstract)
Wang, Z., Huang, C., & Zhu, Q. (2022). Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding (Extended Abstract). In IJCAI International Joint Conference on Artificial Intelligence Vol. 2023-August (pp. 6498-6503).
Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding
Wang, Z., Huang, C., & Zhu, Q. (2022). Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding. Retrieved from http://arxiv.org/abs/2203.14141v1
Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding
Wang, Z., Huang, C., & Zhu, Q. (2022). Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding. In PROCEEDINGS OF THE 2022 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2022) (pp. 1087-1092). Retrieved from https://www.webofscience.com/
Physics-Aware Safety-Assured Design of Hierarchical Neural Network based Planner
Liu, X., Huang, C., Wang, Y., Zheng, B., & Zhu, Q. (2022). Physics-Aware Safety-Assured Design of Hierarchical Neural Network based Planner. In 2022 13TH ACM/IEEE INTERNATIONAL CONFERENCE ON CYBER-PHYSICAL SYSTEMS (ICCPS 2022) (pp. 137-146). doi:10.1109/ICCPS54341.2022.00019
2021
Cocktail: Learn a Better Neural Network Controller from Multiple Experts via Adaptive Mixing and Robust Distillation
Wang, Y., Huang, C., Wang, Z., Xu, S., Wang, Z., & Zhu, Q. (2021). Cocktail: Learn a Better Neural Network Controller from Multiple Experts via Adaptive Mixing and Robust Distillation. In 2021 58th ACM/IEEE Design Automation Conference (DAC) (pp. 397-402). IEEE. doi:10.1109/dac18074.2021.9586148
Cross-Layer Adaptation with Safety-Assured Proactive Task Job Skipping
Wang, Z., Huang, C., Kim, H., Li, W., & Zhu, Q. (2021). Cross-Layer Adaptation with Safety-Assured Proactive Task Job Skipping. ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS, 20(5). doi:10.1145/3477031
Cross-Layer Design of Automotive Systems
Wang, Z., Liang, H., Huang, C., & Zhu, Q. (2021). Cross-Layer Design of Automotive Systems. IEEE DESIGN & TEST, 38(5), 8-16. doi:10.1109/MDAT.2020.3037561
Bounding Perception Neural Network Uncertainty for Safe Control of Autonomous Systems
Wang, Z., Huang, C., Wang, Y., Hobbs, C., Chakraborty, S., & Zhu, Q. (2021). Bounding Perception Neural Network Uncertainty for Safe Control of Autonomous Systems. In 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) (pp. 1745-1750). IEEE. doi:10.23919/date51398.2021.9474204
Safety-Assured Design and Adaptation of Learning-Enabled Autonomous Systems
Zhu, Q., Huang, C., Jiao, R., Lan, S., Liang, H., Liu, X., . . . Xu, S. (2021). Safety-Assured Design and Adaptation of Learning-Enabled Autonomous Systems. In Proceedings of the 26th Asia and South Pacific Design Automation Conference (pp. 753-760). ACM. doi:10.1145/3394885.3431623
2020
Energy-Efficient Control Adaptation with Safety Guarantees for Learning-Enabled Cyber-Physical Systems
Wang, Y., Huang, C., & Zhu, Q. (2020). Energy-Efficient Control Adaptation with Safety Guarantees for Learning-Enabled Cyber-Physical Systems. Retrieved from http://dx.doi.org/10.1145/3400302.3415676
Know the unknowns
Zhu, Q., Li, W., Kim, H., Xiang, Y., Wardega, K., Wang, Z., . . . Choi, H. (2020). Know the unknowns. In Proceedings of the 39th International Conference on Computer-Aided Design (pp. 1-9). ACM. doi:10.1145/3400302.3415768
Divide and Slide: Layer-Wise Refinement for Output Range Analysis of Deep Neural Networks
Huang, C., Fan, J., Chen, X., Li, W., & Zhu, Q. (2020). Divide and Slide: Layer-Wise Refinement for Output Range Analysis of Deep Neural Networks. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 39(11), 3323-3335. doi:10.1109/TCAD.2020.3013071
Opportunistic Intermittent Control with Safety Guarantees for Autonomous Systems
Huang, C., Xu, S., Wang, Z., Lan, S., Li, W., & Zhu, Q. (2020). Opportunistic Intermittent Control with Safety Guarantees for Autonomous Systems. In 2020 57th ACM/IEEE Design Automation Conference (DAC) (pp. 1-6). IEEE. doi:10.1109/dac18072.2020.9218742
Opportunistic Intermittent Control with Safety Guarantees for Autonomous Systems
Huang, C., Xu, S., Wang, Z., Lan, S., Li, W., & Zhu, Q. (2020). Opportunistic Intermittent Control with Safety Guarantees for Autonomous Systems. Retrieved from http://arxiv.org/abs/2005.03726v1
Navigating Discrete Difference Equation Governed WMR by Virtual Linear Leader Guided HMPC
Huang, C., Chen, X., Tang, E., He, M., Bu, L., Qin, S., & Zeng, Y. (2020). Navigating Discrete Difference Equation Governed WMR by Virtual Linear Leader Guided HMPC. In 2020 IEEE International Conference on Robotics and Automation (ICRA) (pp. 151-157). IEEE. doi:10.1109/icra40945.2020.9197375
Efficient System Verification with Multiple Weakly-Hard Constraints for Runtime Monitoring
Wu, S. -L., Bai, C. -Y., Chang, K. -C., Hsieh, Y. -T., Huang, C., Lin, C. -W., . . . Zhu, Q. (2020). Efficient System Verification with Multiple Weakly-Hard Constraints for Runtime Monitoring. In Unknown Book (Vol. 12399, pp. 497-516). doi:10.1007/978-3-030-60508-7_28
ReachNN*: A Tool for Reachability Analysis of Neural-Network Controlled Systems
Fan, J., Huang, C., Chen, X., Li, W., & Zhu, Q. (2020). ReachNN*: A Tool for Reachability Analysis of Neural-Network Controlled Systems. In Unknown Conference (pp. 537-542). Springer International Publishing. doi:10.1007/978-3-030-59152-6_30
SAW: A Tool for Safety Analysis of Weakly-Hard Systems
Huang, C., Chang, K. -C., Lin, C. -W., & Zhu, Q. (2020). SAW: A Tool for Safety Analysis of Weakly-Hard Systems. In Unknown Book (Vol. 12224, pp. 543-555). doi:10.1007/978-3-030-53288-8_26
2019
Towards Verification-Aware Knowledge Distillation for Neural-Network Controlled Systems: Invited Paper
Fan, J., Huang, C., Li, W., Chen, X., & Zhu, Q. (2019). Towards Verification-Aware Knowledge Distillation for Neural-Network Controlled Systems: Invited Paper. In 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) (pp. 1-8). IEEE. doi:10.1109/iccad45719.2019.8942059
ReachNN: Reachability Analysis of Neural-Network Controlled Systems
Huang, C., Fan, J., Li, W., Chen, X., & Zhu, Q. (2019). ReachNN: Reachability Analysis of Neural-Network Controlled Systems. Retrieved from http://arxiv.org/abs/1906.10654v1
Formal verification of weakly-hard systems
Huang, C., Li, W., & Zhu, Q. (2019). Formal verification of weakly-hard systems. In Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control (pp. 197-207). ACM. doi:10.1145/3302504.3311811
Exploring weakly-hard paradigm for networked systems
Huang, C., Wardega, K., Li, W., & Zhu, Q. (2019). Exploring weakly-hard paradigm for networked systems. In Proceedings of the Workshop on Design Automation for CPS and IoT (pp. 51-59). ACM. doi:10.1145/3313151.3313165
2018
Design Automation for Intelligent Automotive Systems
Lan, S., Huang, C., Wang, Z., Liang, H., Su, W., & Zhu, Q. (2018). Design Automation for Intelligent Automotive Systems. In 2018 IEEE International Test Conference (ITC) (pp. 1-10). IEEE. doi:10.1109/test.2018.8624723
2017
Probabilistic Safety Verification of Stochastic Hybrid Systems Using Barrier Certificates
Huang, C., Chen, X., Lin, W., Yang, Z., & Li, X. (2017). Probabilistic Safety Verification of Stochastic Hybrid Systems Using Barrier Certificates. In ACM Transactions on Embedded Computing Systems Vol. 16 (pp. 1-19). Association for Computing Machinery (ACM). doi:10.1145/3126508
Switched Linear Multi-Robot Navigation Using Hierarchical Model Predictive Control
Huang, C., Chen, X., Zhang, Y., Qin, S., Zeng, Y., & Li, X. (2017). Switched Linear Multi-Robot Navigation Using Hierarchical Model Predictive Control. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (pp. 4331-4337). International Joint Conferences on Artificial Intelligence Organization. doi:10.24963/ijcai.2017/605
2016
Family patronage, institutional patronage, and work family conflict: women’s employment status and subjective well-being in urban China
Wu, Y., Wang, P., & Huang, C. (2016). Family patronage, institutional patronage, and work family conflict: women’s employment status and subjective well-being in urban China. The Journal of Chinese Sociology, 3(1). doi:10.1186/s40711-016-0041-2
Tool for analyzing interference problems in aspect-oriented designs
Chen, X., Huang, C., Zhang, Y. F., & Mei, Y. M. (2016). Tool for analyzing interference problems in aspect-oriented designs. Ruan Jian Xue Bao Journal of Software, 27(3), 633-644. doi:10.13328/j.cnki.jos.004985
A Linear Programming Relaxation Based Approach for Generating Barrier Certificates of Hybrid Systems
Yang, Z., Huang, C., Chen, X., Lin, W., & Liu, Z. (2016). A Linear Programming Relaxation Based Approach for Generating Barrier Certificates of Hybrid Systems. In Unknown Conference (pp. 721-738). Springer International Publishing. doi:10.1007/978-3-319-48989-6_44
2015
Method of automatic test case generation for safety-critical scenarios in train control systems
Chen, X., Jiang, P., Zhang, Y. F., Huang, C., & Zhou, Y. (2015). Method of automatic test case generation for safety-critical scenarios in train control systems. Ruan Jian Xue Bao Journal of Software, 26(2), 269-278. doi:10.13328/j.cnki.jos.004780
Research on reliability and correctness assurance methods and techniques for device drivers
Zhang, Y. F., Huang, C., Ou, J. S., Tang, E. Y., & Chen, X. (2015). Research on reliability and correctness assurance methods and techniques for device drivers. Ruan Jian Xue Bao Journal of Software, 26(2), 239-253. doi:10.13328/j.cnki.jos.004778