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About

Lanqing Li is a rising researcher at the intersection of artificial intelligence and chemistry, whose work is pioneering the automation of scientific discovery. Li’s primary research areas span AI for chemistry, reinforcement learning, and human-in-the-loop decision-making. Their most notable contribution is **Chemist-X**, a large language model-empowered agent that revolutionizes reaction condition optimization in chemical synthesis. By leveraging retrieval-augmented generation (RAG), Chemist-X automates a traditionally labor-intensive process, offering a promising future for autonomous chemical reactions—a paper that has already garnered 10 citations since its 2023 publication. In reinforcement learning, Li has made significant strides in addressing the sparse reward and sample inefficiency challenges of multi-goal RL. Their work on **Multi-Step Hindsight Experience Replay with Bias Reduction** (2021, 2023) provides a refined approach to goal relabeling, enhancing planning and robot manipulation tasks. Additionally, Li’s research on **Deploying Offline Reinforcement Learning with Human Feedback** (2023) tackles the critical gap between offline training and safe online deployment, incorporating human guidance to mitigate risks. With a focus on bridging algorithmic innovation and real-world application, Li’s work is shaping the future of autonomous systems in both digital and physical domains.