RL-MIND Lab is a part of the R&L group at Nanjing University. Our research focuses on brain-inspired artificial intelligence, efficient generative AI systems, and multimodal foundation models. The lab's current projects include brain-inspired learning mechanisms and large-scale intelligent systems.
Members of RL-MIND Lab seek to bridge the gap between artificial and natural intelligence, contributing to the development of trustworthy and sustainable AI technologies. We actively publish in top-tier venues such as CVPR, ICML, NeurIPS, ICLR, and ICCV.
- Brain-inspired Artificial Intelligence: Leveraging few-shot concept learning, continual learning, and brain-inspired cognitive computation models for efficient intelligence.
- Efficient Generative AI System: Adapting hardware-software codesign, model compression, and LLM quantization for robotic applications.
- Multimodal Foundation Models: Enabling the deployment and acceleration of large-scale models on edge devices.
We contribute to the community with practical and research-oriented toolkits:
- LibFewShot: An open-source toolkit for few-shot learning.
- LibContinual: An open-source continual learning toolbox.
We are always looking for postdoctoral fellows, PhD students, master's students, and interns. If you are passionate about exploring novel learning paradigms and cognitive-inspired computation, we warmly welcome you to join our team.