2014年-2019年，自动化系，获工学博士学位
2017年-2019年，美国密西根大学，访问学生
2010年-2014年，清华大学，自动化系，获工学学士学位
2025年-至今，清华大学自动化系，副教授
2022年-2025年，清华大学自动化系，助理教授
2021年-2022年，美国密西根大学，助理研究员
2019年-2021年，美国密西根大学，博士后
2022年至今，IEEE Transactions on Intelligent Vehicles，Associate Editor
2021年至今，Automotive Innovation， Academic Editor
中国人工智能学会智能交通专委会副秘书长
从事具身智能系统世界模型、测试验证与安全学习研究，围绕高价值数据在高维变量空间中的极端稀疏性问题，提出了物理智能（Physical AI）的“稀疏度灾难（Curse of Rarity, CoR）”理论，并开创了密集强化学习（Dense Deep Reinforcement Learning, D2RL）和密集学习（Dense Learning）解法，实现了从“AI测试AI”到“AI训练AI”的技术路线创新，系统性解决复杂物理智能系统中高价值数据稀疏分布、难以训练的问题，相关成果在自动驾驶汽车、人形机器人等领域得到广泛应用。
国家重点研发计划项目，项目负责人，中巴无人系统及智能装备联合实验室，2026-2029 国家科技创新专项，项目负责人，雄安数字城市环境下交通世界模型生成与自动驾驶加速测试技术，2025-2028
国家自然科学基金面上项目，项目负责人，智能安全攸关系统等效加速测试理论与方法研究，2025-2028
国家重点研发计划项目，子课题负责人，高级别自动驾驶复杂行车环境风险认知、量化评估与安全决策技术，2023-2026
北京市自然科学基金创新联合基金项目，课题负责人，隐式状态与显式视觉统一的多智能体世界模型关键技术研究，2025-2027
北京市科技新星计划，项目负责人，面向自动驾驶汽车的大模型安全性研究，2024-2026
北京市科技新星计划，项目负责人，车路协同环境下自动驾驶汽车自适应等效加速测试方法研究，2023-2026
2025年，中国自动化学会青年科技奖
2024年，达摩院青橙奖
2024年，清华大学“学术新人奖”
2024年，MIT TR35（中国区）
2023年，国家高层次青年人才
2023年，北京市科技新星
2023年，世界人工智能大会云帆奖·璀璨明星
2021年，INFORMS学会智能交通系统年度最佳论文奖
2020年，IEEE智能交通系统学会最佳博士学位论文奖
2020年，清华大学优秀博士论文
2020年，清华大学优秀毕业生
l Feng, S., Zhu, H., Sun, H., Yan, X., He, L., Yang, J., Su, G., Li, B., Li, S., Wang, L., Shen, S. and Liu, H.X. Breaking through safety performance stagnation in autonomous vehicles with dense learning. Nature Communications (2026). https://doi.org/10.1038/s41467-026-69761-x
l Fan, L., He, L., Ji, H. and Feng, S*. Efficient Safety Verification of Autonomous Vehicles with Neural Network Operator. IFAC World Congress (2026).
l Lu, Q., Wang, X., Jiang, Y., Zhao, G., Ma, M.* and Feng, S*. OmniTester: Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles. Automotive Innovation (2025).
l Yang, J., Bai, R., Ji, H., Zhang, Y., Hu, J. and Feng, S.* Adaptive testing environment generation for connected and automated vehicles with dense reinforcement learning. IEEE Transactions on Intelligent Transportation Systems (2025). https://doi.org/10.1109/TITS.2025.3535866
l Lu, H., Zhu, M., Lu, C., Feng, S., Wang, X., Wang, Y. and Yang, H. Empowering safer socially sensitive autonomous vehicles using human-plausible cognitive encoding. Proceedings of the National Academy of Sciences 122(21), e2401626122 (2025).
l Yan, X., Feng, S.*, Sun, H. and Liu, H.X.* Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing. IEEE Transactions on Intelligent Transportation Systems (2025). https://doi.org/10.1109/TITS.2025.3571966
l Ren, K., Yang, J., Lu, Q., Zhang, Y., Hu, J.* and Feng, S.* Intelligent testing environment generation for autonomous vehicles with implicit distributions of traffic behaviors. Transportation Research Part C: Emerging Technologies (2025). https://doi.org/10.1016/j.trc.2025.105106
l Yang, L., Liu, S., Feng, S.*, Wang, H., Zhao, X., Qu, G. and Fang, S. Generation of critical pedestrian scenarios for autonomous vehicle testing. Accident Analysis & Prevention 214, 107962 (2025).
l Yan, X., Feng, S.*, LeBlanc, D. J., Flannagan, C. and Liu, H. X*. Evaluation of automated driving system safety metrics with logged vehicle trajectory data. IEEE Transactions on Intelligent Transportation Systems (2024). https://doi.org/10.1109/TITS.2024.3397849
l Liu, H.X.* and Feng, S*. Curse of rarity for autonomous vehicles. Nature Communications 15, 4808 (2024). [Featured Article] https://doi.org/10.1038/s41467-024-49194-0
l Bai, R., Yang, J., Gong, W., Zhang, Y., Lu, Q. and Feng, S.* Accurately Predicting Probabilities of Safety-Critical Rare Events for Intelligent Systems. Proceedings of the 2024 IEEE International Conference on Automation Science and Engineering (CASE), 2024.
l Feng, S., Sun, H., Yan, X. et al. Dense reinforcement learning for safety validation of autonomous vehicles. Nature 615, 620-627 (2023). [Cover Article] [ESI Highly Cited Paper] https://doi.org/10.1038/s41586-023-05732-2 (Tsinghua Press Release: https://www.tsinghua.edu.cn/info/1175/102314.htm, 光明日报：https://tech.gmw.cn/2023-04/12/content_36491293.htm, Nature News, Nature Podcast, Nature Videos, TechXplore, ScienceDaily, More Media Coverage：https://nature.altmetric.com/details/144188044/news)
l Yang, J., Sun, H., He, H., Zhang, Y., Liu, H.X. and Feng, S.* Adaptive safety evaluation for connected and automated vehicles with sparse control variates. IEEE Transactions on Intelligent Transportation Systems (2023). https://doi.org/10.1109/TITS.2023.3317078
l Yan, X., Zou, Z., Feng, S., Zhu, H., Sun, H. and Liu, H.X. Learning naturalistic driving environment with statistical realism. Nature Communications 14(1), 2037 (2023). [Featured Article]
l Feng, S., Song, Z., Li, Z., Zhang, Y. and Li, L. Robust cooperative platoon control in mixed traffic flow based on Tube Model Predictive Control. IEEE Transactions on Intelligent Vehicles 6(4), 711-722 (2021). https://doi.org/10.1109/TIV.2021.3060626
l Pei, H., Zhang, Y., Zhang, Y. and Feng, S.* Optimal cooperative driving at signal-free intersections with polynomial-time complexity. IEEE Transactions on Intelligent Transportation Systems (2021). https://doi.org/10.1109/TITS.2021.3118592
l Feng, S., Yan, X., Sun, H., Feng, Y. and Liu, H.X. Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment. Nature Communications 12, 748 (2021). [Featured Article] [ITS Outstanding Paper Award] [ESI Highly Cited Paper] https://www.nature.com/articles/s41467-021-21007-8
l Feng, S., Feng, Y., Yu, C., Zhang, Y. and Liu, H.X. Testing scenario library generation for connected and automated vehicles, Part I: Methodology. IEEE Transactions on Intelligent Transportation Systems 22(3), 1573-1582 (2020).
l Feng, S., Feng, Y., Sun, H., Bao, S., Zhang, Y. and Liu, H.X. Testing scenario library generation for connected and automated vehicles, Part II: Case studies. IEEE Transactions on Intelligent Transportation Systems 22(9), 5635-5647 (2020).
l Feng, S., Feng, Y., Sun, H., Zhang, Y. and Liu, H.X. Testing scenario library generation for connected and automated vehicles: An adaptive framework. IEEE Transactions on Intelligent Transportation Systems 23(2), 1213-1222 (2020).
l Feng, S., Feng, Y., Yan, X., Shen, S., Xu, S. and Liu, H.X. Safety assessment of highly automated driving systems in test tracks: A new framework. Accident Analysis & Prevention 144, 105664 (2020).
l Pei, H., Feng, S.*, Zhang, Y. and Yao, D. A cooperative driving strategy for merging at on-ramps based on dynamic programming. IEEE Transactions on Vehicular Technology 68(12), 11646-11656 (2019).
l Feng, S., Zhang, Y., Li, S.E., Cao, Z., Liu, H.X. and Li, L. String stability for vehicular platoon control: Definitions and analysis methods. Annual Reviews in Control 47, 81-97 (2019). [ESI Highly Cited Paper]
