教师名录
个人简介
顾尚定博士，上海交通大学计算机学院长聘教轨副教授、博士生导师，入选国家级青年人才计划。 2024 年获德国慕尼黑工业大学（Technical University of Munich）计算机科学博士学位，博士毕业后，于美国加州大学伯克利分校（UC Berkeley）从事博士后研究，开展安全智能决策及其在工业场景中的应用研究。研究聚焦于强化学习、AI 安全、Agentic AI、具身智能与机器人学习，重点探索如何构建安全、可靠、高效且能够持续学习与自主决策的智能系统，并推动相关技术在大语言模型、多模态智能体、机器人、自动化系统及半导体制造等真实场景中的应用。相关成果发表于 Science Advances、IEEE TPAMI、Artificial Intelligence、IEEE TASE、NeurIPS、ICLR 等国际顶级期刊与会议。已获授权发明专利 10 项，其中中国发明专利 9 项、美国发明专利 1 项。相关研究受到《经济学人》（The Economist）、《IEEE Spectrum》等国际媒体报道。曾获 OpenAI、NVIDIA、Lam Research等机构科研支持，并担任 NeurIPS、IJCNN等会议 Area Chair、IEEE TASE 专刊首席客座编辑、多个会议的程序委员会委员，以及多个国际研讨会和学术活动的组织者。更多信息请见实验室主页：https://sail-research-lab.github.io
论文发表
近5年相关代表文章
Shangding Gu, Bilgehan Sel, Yuhao Ding, Lu Wang, Qingwei Lin, Alois Knoll, and Ming Jin. (2025). Safe and Balanced: A Framework for Constrained Multi-Objective Reinforcement Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Shangding Gu, Laixi Shi, Muning Wen, Ming Jin, Eric Mazumdar, Yuejie Chi, Adam Wierman, and Costas Spanos. (2025). Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning. ICLR 2025.
Shangding Gu, Long Yang, Yali Du, Guang Chen, Florian Walter, Jun Wang, and Alois Knoll. (2024). A Review of Safe Reinforcement Learning: Methods, Theories, and Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Shangding Gu, Laixi Shi, Yuhao Ding, Alois Knoll, Costas Spanos, Adam Wierman, and Ming Jin. (2024). Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulation. NeurIPS 2024.
Shangding Gu, Alois Knoll, and Ming Jin. (2024). TeaMs-RL: Teaching LLMs to Generate Better Instruction Datasets via Reinforcement Learning. Transactions on Machine Learning Research.
Shangding Gu, Puze Liu, Alap Kshirsagar, Guang Chen, Jan Peters, and Alois Knoll. (2024). ROSCOM: Robust Safe Reinforcement Learning on Stochastic Constraint Manifolds. IEEE Transactions on Automation Science and Engineering.
Shangding Gu, Dianye Huang, Muning Wen, Guang Chen, and Alois Knoll. (2024). Safe Multiagent Learning With Soft Constrained Policy Optimization in Real Robot Control. IEEE Transactions on Industrial Informatics.
Shangding Gu, Bilgehan Sel, Yuhao Ding, Lu Wang, Qingwei Lin, Ming Jin, and Alois Knoll. (2024). Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient Manipulation. AAAI 2024 (Oral).
Shangding Gu, Jakub Grudzien Kuba, Yuanpei Chen, Yali Du, Long Yang, Alois Knoll, and Yaodong Yang. (2023). Safe Multi-Agent Reinforcement Learning for Multi-Robot Control. Artificial Intelligence.
Zhi Zheng and Shangding Gu. (2024). Safe Multi-Agent Reinforcement Learning with Bilevel Optimization in Autonomous Driving. IEEE Transactions on Artificial Intelligence.
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