Shangding Gu

Shangding Gu, Dr. rer. nat.

E-Mail	shangding.gu(at)cs.tum.edu
Address	Boltzmannstr. 3 85748 Garching b. München Gemany
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 current research focuses on reinforcement learning, planning, and AI safety, with applications in foundation models (e.g., large language models and multi-modal models), robotics, and semiconductor manufacturing. My goal is to design safe, reliable, and efficient systems that address pressing real-world challenges and drive impactful applications across diverse domains. My work has been featured in leading publications, including top-tier journals and conferences such as The Journal of Artificial Intelligence, IEEE Transactions on Pattern Analysis and Machine Intelligence, NeurIPS, and other prestigious venues.

See Personal Homepage

See Google Scholar

See Personal Github / SafeRL-Lab Github

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.