Shangding Gu, M.Sc.

Address Boltzmannstr. 3
85748 Garching b. München
Office hours appointment by email

Curriculum Vitae

I am a guest researcher at the Chair of Robotics, Artificial Intelligence and Real-time Systems. I had a great time visiting Prof. Jan Peters' lab from September 2022 to December 2022. Following this, I did a research internship at Microsoft from April 2023 to August 2023. My research currently focuses on developing artificial intelligence methods and models, with a special interest in exploring the theory of safe reinforcement learning and motion planning, and its application in robotics, in which the goal is to enable robots to know how to learn, reason and plan, and enable robots to work in support of people.

Research Interests

  • Safe/Robust Reinforcement Learning; Reinforcement Learning Theory; AI Safety.
  • Motion Planning; Autonomous Driving; Robotics (e.g., arm robotics and marine robotics). 

Trustworthy Interactive Decision-Making with Foundation Models Workshop

We are organizing a Trustworthy Interactive Decision-Making with Foundation Models Workshop at IJCAI 2024, the researchers and students who are interested in trustworthy AI  are welcome to join us! Call for contributions.

Safe Reinforcement Learning Workshop

We organized a safe reinforcement learning workshop, the researchers and students who are interested in safe RL  are welcome to join us! The recorded videos are available on YouTube's Safe RL Channel, please see the YouTube Channel or  Workshop Homepage.

Safe Reinforcement Learning Online Seminar

In December 2022, we launched a long-term safe reinforcement learning online seminar. Every month, we will invite at least one speaker to share cutting-edge research with RL researchers and students (each speaker has about 1 hour to share his/her research). We believe that holding this seminar can  promote the research of safe reinforcement learning. For details, please see the Seminar Homepage.

Offered Thesis Topics

Ongoing Master Thesis Topics:

  • Stability analysis of safe reinforcement learning 
  •  A safe reinforcement learning method based on control theory
  • Privacy Risk Analysis for Synthetic Data

Finished Guided Research topic:

  •  A safe multi-agent reinforcement learning algorithm in robotics applications  

If you are interested in the above topics, please feel free to contact me indicating your background and skills.

Supervised Students

Kathleen Baur (Now at Cornell University)

Mhamed Jaafar (Now at Brainlab)

Zheng Zhi (Now at Agile Robots AG)

Jiarui Zou (Now at TUM)

Donghao Song (Collaborated with Derui Zhu)

Academic Service as a Reviewer

As a reviewer for some international conferences and journals:

  • ICML 2021, NeurIPS 2021, ICLR 2021, AAAI 2021
  • ICML 2022, NeurIPS 2022, ICLR 2022, AAAI 2022, ICRA 2022, IROS 2022, AAMAS 2022 
  • ICML 2023, NeurIPS 2023, ICLR 2023, AAAI 2023, ICRA 2023, IROS 2023
  • ICML 2024, NeurIPS 2024, ICLR 2024, AAAI 2024, ICRA 2024, IROS 2024
  • Journal of Machine Learning Research
  • IEEE Transactions on Automation Science and Engineering
  • IEEE Transactions on Vehicular Technology
  • IEEE Transactions on Neural Networks and Learning Systems
  • IEEE Transactions on Intelligent Transportation Systems
  • IEEE Transactions on Artificial Intelligence
  • IEEE Transactions on Aerospace and Electronic Systems
  • IEEE Transactions on Cognitive and Developmental Systems
  • IEEE Robotics and Automation Letters
  • IEEE Transactions on Intelligent Vehicles
  • IEEE Access
  • Journal of Navigation
  • Ocean Engineering
  • Applied Ocean Research
  • Journal of Engineering for the Maritime Environment
  • Intelligent Automation & Soft Computing, etc.