Congratulations to three faculty members in the Department of Data Science who have captured the highly competitive 2025 National Natural Science Foundation of China’s (NSFC) Fund for their wide-ranging research projects. This year, a record-high 56 scholars from CityUHK received this funding.
Professor Xiangyu ZHAO – Young Scientists Fund (Category C)
Associate Professor, Department of Data Science
Project: Research on Key Technologies of Adaptive Recommendation Based on Automated Machine Learning (基於自動機器學習的自適應推薦關鍵技術研究)
Scope: This project develops key technologies for adaptive recommender systems via automated machine learning. It proposes novel frameworks for dynamic feature selection, embedding dimension search, automated interaction design, and reinforcement learning–based optimization, enabling rapid deployment and improved effectiveness of recommendation technologies across industries, with strong academic and industrial impact expected.
Professor Xingyu GUO – Young Scientists Fund (Category C)
Global Research Assistant Professor, Department of Data Science
Project: AI-Driven Design and Investigation of Electrochemical Mechanisms in Li-Excess Cathode Materials (基於人工智能的富鋰正極材料設計與電化學機理研究)
Scope: This project integrates first-principles calculations and AI to investigate composition, phase stability, structural evolution, and kinetics of lithium-excess layered oxides, aiming to uncover redox–performance relationships and identify novel cathodes with high energy density, fast kinetics, and stable cycling for next-generation lithium-ion batteries.
Professor Zimu ZHOU – General Programme
Assistant Professor, Department of Data Science
Project: Research on Personalized Federated Learning for Multi-Source Edge Big Data (面向多源端側大數據的個人化聯邦學習方法研究)
Scope: This project develops key technologies for personalized federated learning over multi-source edge data in domains such as smart transportation and smart cities. It jointly addresses challenges of data and system heterogeneity through federated clustering, personalized distillation, and asynchronous aggregation. It will establish a comprehensive framework to support efficient and effective cross-domain data collaboration with edge devices.
These distinguished grants affirm the exceptional research calibre of our faculty. We warmly congratulate Professor ZHOU, Professor ZHAO and Professor GUO and wish them continued success.