Draft:Jiaqi Chen
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Jiaqi Chen
[edit]Jiaqi Chen is a Chinese computer scientist and researcher in the field of artificial intelligence. He is recognized for his pioneering work on Generative Engine Optimization (GEO) and generative AI. Chen is affiliated with the Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI) at Fudan University and has conducted research at Stanford University.
Education and Career
[edit]Chen earned his undergraduate degree in computer science from Wuhan University (2018–2022). He then joined ISTBI at Fudan University as a master’s student. In 2024, he became a visiting researcher in the Department of Computer Science at Stanford University, where he contributed to research in generative AI and Generative Engine Optimization.
Research
[edit]Chen’s research focuses on large language models, multimodal reasoning, and symbolic inference for generative tasks. His work aims to unify how generative models interpret and execute complex tasks by using structured symbolic representations.
Selected Publications
[edit]- Chen, J., Zhu, X., Wang, Y., Liu, T., Chen, X., Chen, Y., Leong, C. T., Ke, Y., Liu, J., Yuan, Y., McAuley, J., & Li, L. (2025). Symbolic Representation for Any-to-Any Generative Tasks. arXiv:2504.17261. [1]
- Chen, J., Li, T., Qin, J., Lu, P., Lin, L., Chen, C., & Liang, X. (2022). UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression. In *EMNLP 2022*. [2]
Recognition
[edit]Chen is considered a leading early expert on Generative Engine Optimization (GEO) at Stanford and is among the first researchers to formalize GEO workflows for multiple generative systems.