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Assistant Professor

Yongqi ZHANG

Data Science and Analytics Thrust, HKUST (Guangzhou)

PhD in CSE, HKUST (2020) ยท BS, SJTU (2015)

Multimodal ReasoningRAG SystemsLLM AgentsAI4Science

Designing intelligent systems that reason with knowledge, retrieval, and scientific structure.

Yongqi Zhang is currently a tenure-track Assistant Professor in the Data Science and Analytics Thrust at HKUST(GZ). His research focuses on multimodal reasoning, RAG systems, LLM agents, and AI4Science. Before joining HKUST(GZ), he worked as a research scientist at 4Paradigm and collaborated closely with Prof. Quanming Yao at Tsinghua University.

In terms of education, Dr. Zhang completed his doctoral degree in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST) in 2020, advised by Prof. Lei Chen. Before pursuing his Ph.D., he obtained a bachelor's degree from Shanghai Jiao Tong University (SJTU) in 2015. With this background, Dr. Zhang possesses extensive academic and industry experience, as well as strong collaborations with partners.

40+

Publications

4

Research Themes

2024

KiMI Founded

Research Themes

What We Work On

We build AI systems that can understand, retrieve, reason, and act across modalities and domains by integrating data-driven learning with domain knowledge.

Multimodal Reasoning

Understanding and reasoning across vision, language, and structured knowledge.

RAG Systems

Multimodal search, retrieval, ranking, and grounded generation over large-scale corpora.

LLM Agents

Planning, memory, tool use, verification, and multi-agent collaboration for complex problem solving.

AI for Science

Knowledge-enhanced foundation models and intelligent systems for biology, materials, and engineering.

Lab

KiMI Lab

KiMI is dedicated to Knowledge-integrated Machine Intelligence, with a focus on building AI systems that understand, retrieve, reason, and act across modalities and domains.

Recruitment

Join the Group

The group is actively looking for students and collaborators interested in Multimodal Reasoning, RAG Systems, LLM Agents, and AI for Science.