About Me
Enjun Du is currently an undergraduate student in the Class of 2022 at the School of Cyberspace Science and Technology, Beijing Institute of Technology. His research interests are knowledge-integrated large language models and scientific artificial intelligence. Enjun Du is currently a research assistant at KiMI Lab, The Hong Kong University of Science and Technology (Guangzhou), under the supervision of Prof.Yongqi Zhang. At the same time, Du Enjun is also a research assistant in the lab of Prof. Ronghua Li at Beijing Institute of Technology. Previously, he was a research intern in the group of Prof. Zhida Qin. This has given Enjun Du extensive research experience.
I am always dedicated to discussing problems with people who share the same passion for research. If you are interested in my work or would like to explore interesting research ideas with me, please feel free to contact me via my email!
News: I will be visiting the KiMI Lab at the Hong Kong University of Science and Technology (Guangzhou Campus) as a visiting student for academic exchanges from June 25, 2025 to June 25, 2026. I’m really looking forward to making friends with HKUST-GZ alumni during my stay!
Email:
- enjundu.cs@gmail.com (Primary)
- edu719@connect.hkust-gz.edu.cn (optional)
- enjun_du@bit.edu.cn (Not recommended, I won’t be able to use it after June 2026)
- AI-Driven Science
- Data-Centric Learning
- Knowledge-Integrated Large Language Models
Research Assistant in KiMI lab
The Hong Kong University of Science and Technology (Guangzhou)
Research Assistant in Ronghua Li's Lab
Beijing Institute of Technology
Research Assistant in Zandar Qin's Lab
Beijing Institute of Technology
Bachelor of Cyberspace Science and Technology
Beijing Institute of Technology
My current research interests are:
AI-Driven Science: Leveraging advanced artificial intelligence techniques to tackle complex scientific challenges, this research promotes interdisciplinary innovation and discovery that unveil novel scientific insights.
Data-Centric Learning: Integrating universal graph models with large language models to significantly enhance data quantity, quality, efficiency, and privacy. This approach aims to optimize the robustness and scalability of machine learning systems for real-world applications.
Knowledge-Integrated LLMs: Merging domain-specific knowledge with large language models to fortify their reasoning capabilities and adaptability, ultimately striving to develop more efficient and universally applicable intelligent solutions.
Here are some of my research works. If you have any questions about them or would like to connect and explore new ideas together, I welcome the discussion. Please reach out to collaborate! 😃
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Mar 26, 2025
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Mar 25, 2025
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Mar 6, 2025
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Mar 3, 2025
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Mar 2, 2025
Jan 19, 2025
Journals
- TIST in 2024: ACM Transactions on Intelligent Systems and Technology
Conferences
- ICML 2025: International Conference on Machine Learning
- ARR 2025: ACL Rolling Review
Education
Research Assistant in KiMI lab
The Hong Kong University of Science and Technology (Guangzhou)Supervised by Prof.Yongqi ZhangResearch Assistant in Ronghua Li's Lab
Beijing Institute of TechnologySupervised by Prof.Ronghua Li and Dr.Xunkai LiResearch Assistant in Zandar Qin's Lab
Beijing Institute of TechnologySupervised by Prof.Zhida QinBachelor of Cyberspace Science and Technology
Beijing Institute of TechnologyGPA: 91/100 Rank: 5%Read Thesis