The Department of Data Science (DS) has secured almost HK$3.0 million in funding for five research projects under the 2026/27 General Research Fund (GRF) and Early Career Scheme (ECS) announced by the Research Grants Council (RGC). This achievement demonstrates the Department’s continued commitment to advancing the frontiers of data science through high-quality, innovative, and impactful research.
The RGC GRF/ECS exercise is a highly competitive funding scheme that supports research projects at eight UGC-funded universities in Hong Kong. This year’s success reflects the Department’s strong research capability and vibrant research environment across a wide range of emerging areas, including federated analytics, large language models, quantum systems, diffusion models, and information retrieval.
We extend our heartfelt congratulations to our Principal Investigators, including Professor Clint HO, Professor Xiangyu ZHAO, Professor Ning MIAO, Professor Lu YU, and Professor Yang QIAN. Their dedication and research excellence will undoubtedly lead to impactful outcomes, further enhancing the Department’s reputation and contributing to the broader scientific community.
Details of the funded grants are as follows:
| Name of Principle Investigator | Project Title | Grant Awarded | Scheme |
|---|---|---|---|
| Professor Clint HO | Distributionally Robust Federated Analytics | 0.5M | GRF |
| Professor Xiangyu ZHAO | Deep Search Engines in the Era of Large Language Models: Understanding, Adaptiveness, and Robustness | 0.9M | GRF |
| Professor Ning MIAO | Optimizing Mathematical Reasoning in LLMs via Difficulty-Controlled Reinforcement Learning | 0.3M | ECS |
| Professor Lu YU | Advancing the Mathematical Understanding of Score-Based Diffusion Models: Convergence, Acceleration, and Robustness | 0.6M | GRF |
| Professor Yang QIAN | Learning-Based Characterization of Quantum Systems under Resource Constraints | 0.7M | GRF |
Congratulations once again to all the awardees on their remarkable achievements. The Department looks forward to their outstanding research outputs and continued contributions to the development of data science.