CMU Safe AI Lab

Research laboratory at Carnegie Mellon University

The Safe AI Lab at CMU aims to develop reliable, explainable, verifiable, and good-for-all artificial intelligent learning methods for consequential applications in the face of the uncertain, dynamic, time-varying, multiple agents, and possibly human-involved environment by bridging fundamental learning theories and engineering technologies. The lab is recognized nationally and internationally for its research on autonomous vehicles and trustworthy AI. The lab has worked with many industrial partners, including Google Brain, Amazon, Ford, Uber, IBM, Adobe, Bosch, Toyota, and Rolls-Royce.

Positions are available for postdocs, master students, and interns. If you are interested in applying for a position, please fill out this form. It requires your CV and a 3-slide PPT describing your previous experience. For master and interns, we prefer students who have good engineering experience. For PhD application, we prefer students with publication experience. If you already have publication related to our research directions and a good GPA, you can send a reminder emails to zhaolab.jobs@gmail.com, by attaching one of your best papers and your CV. DO NOT repeatedly send me emails or send them to my other emails. It may make your email blocked.

We usually recruit 2 to 3 new Ph.D. students each year and have openings for postdocs targeting on faculty positions in top 20 universities. We recruite people at multiple departments of CMU, including Mechanical Engineering Department, Computer Science Department, and the Robotic Institute.

Selected Recent Publications

Evaluation and Certification

M. Arief, Z. Huang, G. Kumar, Y. Bai, S. He, W. Ding, H. Lam, D. Zhao, ''Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems,'' Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, San Diego, USA, 2021. \| Bib `@article{arief2020deep, title={Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems}, author={Arief, Mansur and Huang, Zhiyuan and Kumar, Guru Koushik Senthil and Bai, Yuanlu and He, Shengyi and Ding, Wenhao and Lam, Henry and Zhao, Ding}, journal={arXiv preprint arXiv:2006.15722}, year={2021} }`
F Wu, L Li, Z Huang, Y Vorobeychik, D Zhao, B Li, ''CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing,'' The International Conference on Learning Representations (ICLR), 2022. \|

M. Arief, Z. Huang, G. Kumar, Y. Bai, S. He, W. Ding, H. Lam, D. Zhao, ''Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems,'' Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, San Diego, USA, 2021. | Bib


								@article{arief2020deep,
								title={Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems},
								author={Arief, Mansur and Huang, Zhiyuan and Kumar, Guru Koushik Senthil and Bai, Yuanlu and He, Shengyi and Ding, Wenhao and Lam, Henry and Zhao, Ding},
								journal={arXiv preprint arXiv:2006.15722},
								year={2021}
								}

F Wu, L Li, Z Huang, Y Vorobeychik, D Zhao, B Li, ''CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing,'' The International Conference on Learning Representations (ICLR), 2022. |

Safe Reinforcment Learning

Z Liu, Z Cen, V Isenbaev, W Liu, ZS Wu, B Li, D Zhao, ''Constrained variational policy optimization for safe reinforcement learning,'' Thirty-ninth International Conference on Machine Learning (ICML), 2022. \|
B. Chen, Z. Liu, J. Zhu, M. Xu, W. Ding, D. Zhao, ''Context-Aware Safe Reinforcement Learning for Non-Stationary Environments,'' 2021 International Conference on Robotics and Automation (ICRA), 2021. \| Bib `@article{chen2021context, title={Context-Aware Safe Reinforcement Learning for Non-Stationary Environments}, author={Chen, Baiming and Liu, Zuxin and Zhu, Jiacheng and Xu, Mengdi and Ding, Wenhao and Zhao, Ding}, journal={2021 International Conference on Robotics and Automation (ICRA)}, year={2021} }`

Z Liu, Z Cen, V Isenbaev, W Liu, ZS Wu, B Li, D Zhao, ''Constrained variational policy optimization for safe reinforcement learning,'' Thirty-ninth International Conference on Machine Learning (ICML), 2022. |

B. Chen, Z. Liu, J. Zhu, M. Xu, W. Ding, D. Zhao, ''Context-Aware Safe Reinforcement Learning for Non-Stationary Environments,'' 2021 International Conference on Robotics and Automation (ICRA), 2021. | Bib


									@article{chen2021context,
				  title={Context-Aware Safe Reinforcement Learning for Non-Stationary Environments},
				  author={Chen, Baiming and Liu, Zuxin and Zhu, Jiacheng and Xu, Mengdi and Ding, Wenhao and Zhao, Ding},
				  journal={2021 International Conference on Robotics and Automation (ICRA)},
				  year={2021}
				}

Generalizable Reinforcment Learning

M. Xu, W. Ding, J. Zhu, Z. Liu, B. Chen, D. Zhao, ''Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes,'' Advances in Neural Information Processing Systems (NeurIPS), 2020. \| Bib @inproceedings{NEURIPS2020_47951a40, author = {Xu, Mengdi and Ding, Wenhao and Zhu, Jiacheng and LIU, ZUXIN and Chen, Baiming and Zhao, Ding}, booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin}, pages = {6429--6440}, publisher = {Curran Associates, Inc.}, title = {Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes}, url = {https://proceedings.neurips.cc/paper/2020/file/47951a40efc0d2f7da8ff1ecbfde80f4-Paper.pdf}, volume = {33}, year = {2020} }
M. Xu, Y. Shen, S. Zhang, Y. Lu, D. Zhao, J, Tenenbaum, C. Gan, ''Prompting Decision Transformer for Few-shot Policy Generalization,'' Thirty-ninth International Conference on Machine Learning (ICML), 2022. \|
W. Ding, H. Lin, B. Li, D. Zhao, ''Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning,'' Advances in Neural Information Processing Systems (NeurIPS), 2022. \|

Survey and Benchmark

M Xu, Z Liu, P Huang, W Ding, Z Cen, B Li, D Zhao, ''Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability,'' arXiv preprint arXiv:2209.08025, 2022. \|
W Ding, C Xu, H Lin, B Li, D Zhao, ''A Survey on Safety-critical Scenario Generation from Methodological Perspective,'' arXiv preprint arXiv:2202.02215, 2022. \|
C Xu, W Ding, W Lyu, Z Liu, S Wang, Y He, H Hu, D Zhao, B Li, ''SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles,'' Advances in Neural Information Processing Systems (NeurIPS), arXiv preprint arXiv:2206.09682, 2022. \|
C Xie, Z Cao, Y Long, D Yang, D Zhao, B Li, ''Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions,'' arXiv preprint arXiv:2209.04022, 2022. \|