Kaixin Zhu / 朱凯鑫
About
I am pursuing an undergraduate degree in Artificial Intelligence at Hong Kong Baptist University, focusing on deep learning, computer vision (4D generation), and foundation models (model design). My current research goals are to advance and extend the applications of spiking neural networks and to explore transfer learning to inspire their greater potential. Additionally, I am further exploring the field of Artificial Intelligence Science, working towards Artificial Intelligent Generated Content. To deepen my research, I joined Dr Wentao Zhang's lab at the International Machine Learning Research Centre at Peking University as an intern.
As an undergraduate student, I am endeavoring to study and explore to continuously improve my knowledge and skills in the field of artificial intelligence. I participate in courses and projects, actively engage in academic discussions, and try collaborating with my peers to expand my research horizons and collaborative skills.
I am passionate about the development of AI science and want to contribute to the development and application of AI technology through continuous learning and practice. I have ambitions to become an influential AI researcher and hope to contribute to solving complex problems in the real world. I believe that through diligence and hard work, I can continue to grow in this rapidly evolving field and contribute positively to future research and innovation.
News
- September 1st, 2024 CCF student member
- July 26th, 2024 IEEE student member
- June 16th, 2024 Won the first prize of the undergraduate group in Cross-Strait and Hong Kong-Macao College Student Computer Innovation Competition
Intern Experience

Research Intern at International Machine Learning Research Center, Peking University
Start Date: March 2024
End Date: Present
Preprints

Trans4D: Realistic Geometry-Aware Transition for Compositional Text-to-4D Synthesis
Bohan Zeng*, Ling Yang*, Siyu Li, Jiaming Liu, Zixiang Zhang, Juanxi Tian, Kaixin Zhu, Yongzhen Guo, Fu-Yun Wang, Minkai Xu, Stefano Ermon, Wentao Zhang
Trans4D is currently accessible on the arXiv platform.
arXiv Code