Jiaqi Chen
Visiting Researcher at Stanford University Department of Computer Science
Palo Alto, California, United States
Summary
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Rockstar🎓
Top SchoolJiaqi Chen is a visiting researcher at Stanford with four years of experience building 3D visual perception systems and LLM-driven generative agents for embodied AI. Their work spans top-tier publications (CVPR, NeurIPS, ICLR) and program committee/reviewer roles across major conferences, reflecting both research depth and community service. Past collaborations include high-impact internships at Alibaba and MetaGPT, contributing to spotlight NeurIPS work and widely adopted open-source projects (MetaGPT/Data-Interpreter) with large citation and star counts. At Stanford and Fudan they focus on neural-symbolic representations and unified multimodal architectures, and have driven widely used tools such as Semantic Segment Anything and S-Agents. Beyond papers, Jiaqi combines practical leaderboard-winning system design (Top-1 on nuScenes) with generative interface research, bridging academic rigor and deployable AI systems.
3 years of coding experience
Bachelor of Engineering - BE, Computer Science, Bachelor of Engineering - BE, Computer Science at Wuhan UniversityMaster of Engineering - MEng, Computer Science, Master of Engineering - MEng, Computer Science at Fudan UniversityVisiting student researcher, Neural symbolic representation, 3D reconstruction, Visiting student researcher, Neural symbolic representation, 3D reconstruction at Stanford University
Github Skills (28)
agentic-workflow10
browser-automation9
agent9
prompt-engineering9
agentic-ai9
llm-inference7
genai7
transfer-learning5
representation-learning5
autotest5
arxiv4
deep-learning3
python3
clip3
composite3
Programming languages (3)
Github contributions (5)
fudan-zvg/GSS
Mar 2023 - Dec 2023
[CVPR 2023] Official repository of Generative Semantic Segmentation
Contributions:1 PR, 78 pushes, 4 branches in 8 months
fudan-zvg/Semantic-Segment-Anything
Apr 2023 - Jun 2023
Automated dense category annotation engine that serves as the initial semantic labeling for the Segment Anything dataset (SA-1B).
Contributions:2 PRs, 33 pushes, 19 comments in 1 month
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