CSE PhD Student SHI Yu-Zhe Awarded NSFC Young Student Basic Research Project

We are pleased to announce that SHI Yu-Zhe, a PhD candidate in the Department of Computer Science and Engineering advised by Prof. QU Huamin, has been awarded the National Natural Science Foundation of China (NSFC) Young Student Basic Research Project (PhD Track) for his project titled "On the Automated Design of Procedural Knowledge Representation: Theories and Algorithms."

This highly competitive scheme provides funding of CNY 300,000 for the period from January 2026 to December 2027. The project is the only HKUST-funded initiative in the general field of Information Sciences under this scheme. As HKUST participates in the program for the first time, this award highlights the University’s growing contribution to high-quality NSFC-supported research.

The project aims to tackle a key challenge in bringing AI into professional domains: how to turn procedural knowledge, the step-by-step expertise behind real-world tasks, into forms that machines can use effectively. Drawing on declarative knowledge, expert consensus and case archives, and building on a bidirectional optimization framework that combines empirical induction with theoretical deduction, the research will develop theories and algorithms for automatically building structured, verifiable and executable knowledge representations. It will also establish evaluation methods and explore how these representations can work with advanced AI models, with demonstration applications in smart manufacturing and scientific discovery.

The project also involves academic collaboration with leading scholars including Prof. WANG Qining of Peking University and Prof. Philip S. Yu of the University of Illinois Chicago.

SHI Yu-Zhe noted, "This project was inspired by a fundamental dilemma in AI development: general AI techniques aim to cover domains broadly, while domain practitioners require deep and specialized knowledge. Under limited computational resources, this creates a trade-off between breadth and depth. My research seeks to address this challenge by disentangling domain-specific knowledge representation from AI techniques, reducing integration complexity and enabling modular knowledge components to be incorporated into AI systems as needed. I hope this work will help make AI more accessible to domain practitioners and support the broader integration of AI across professional fields."

We congratulate SHI Yu-Zhe on this achievement and look forward to his continued research contributions.

SHI Yu-Zhe (center), NSFC Young Student Basic Research Project recipient, with advisor Prof. Huamin Qu (right).