2021 美国密歇根大学,机械工程学院,博士
2016 清华大学,车辆工程系,硕士
2013 清华大学,车辆工程系,学士
2023-至今 上海交通大学溥渊未来技术学院 长聘教轨助理教授
2021-2023 美国福特汽车公司机器人研究所 研究员
智能车/机器人决策控制算法
强化学习/元强化学习
工业具身智能
AI辅助航空发动机设计
N. Rober, M. Everett, S. Zhang, and J. P. How, 'A hybrid partitioning strategy for backward reachability of neural feedback loops', in 2023 American Control Conference (ACC), 2023, pp. 3523–3528.
X. Wang, S. Zhang, and H. Peng, 'Comprehensive safety evaluation of highly automated vehicles at the roundabout scenario', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 20873–20888, 2022.
L. Wen, S. Zhang, H. E. Tseng, B. Singh, D. Filev, and H. Peng, 'Improved Robustness and Safety for Pre-Adaptation of Meta Reinforcement Learning with Prior Regularization', in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 8987–8994.
X. Wang, H. Peng, S. Zhang, and K.-H. Lee, 'An interaction-aware evaluation method for highly automated vehicles', in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2021, pp. 394–401.
M. Zhu, S. Zhang, Y. Zhong, P. Lu, H. Peng, and J. Lenneman, 'Monocular 3d vehicle detection using uncalibrated traffic cameras through homography', in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 3814–3821.
S. Zhang, L. Wen, H. Peng, and H. E. Tseng, 'Quick learner automated vehicle adapting its roadmanship to varying traffic cultures with meta reinforcement learning', in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2021, pp. 1745–1752.
S. Zhang, 'Synthesis and Evaluation of Automated Vehicles', 2021.
S. Zhang, H. Peng, S. Nageshrao, and H. E. Tseng, 'Generating socially acceptable perturbations for efficient evaluation of autonomous vehicles', in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, 2020, pp. 330–331.
S. Zhang, H. Peng, S. Nageshrao, and H. E. Tseng, 'Discretionary Lane Change Decision Making using Reinforcement Learning with Model-based Exploration', in 18th IEEE International Conference on Machine Learning and Applications, 2019, p. 6.
研-ECE6801G-49000-M01-深度学习