The Education University of Hong Kong
Department of Mathematics and Information Technology
Research Cluster on Data Science & Analytics
The research cluster on Data Science & Analytics brings together a research team of scholars in statistics, data science, computing, and big data analytics to advance data-driven knowledge and practice. Our mission is to develop rigorous methods, scalable systems, and impactful applications that turn complex data into actional insights. We focus on two interconnected themes:
- Foundations of Data Science and Artificial Intelligence
We develop methods in statistical modelling, machine learning, time series analysis, optimization, and AI/GenAI tools to address high-dimensional, longitudinal, and complex data, with an emphasis on interpretable and reliable analytics.
- Big Data Infrastructure and Analytics
We design and deploy data infrastructures, pipelines, and analytic frameworks to support large-scale studies in various fields such as education, sports, healthcare and social sciences.
Through collaborative projects, external partnerships, and events such as Seminars, Public Lectures, and the Symposium on Data Science and Analytics (SDSA), the cluster serves as a hub for cutting-edge research, postgraduate training, and knowledge transfer in data science and analytics within Hong Kong and beyond.
Some representative research outputs include:
- Li, Xin, & Yang, Wen (2025). Cyclic scheduling of multi-type wafers concurrent processing in single-arm cluster tools with residency time constraints. Expert Systems with Applications, 269, Article 126443. https://doi.org/10.1016/j.eswa.2025.126443
- Li, Wenjuan, Meng, Weizhi and Chen, Xiaofu (2026). Knowledge-distilled temporal convolutional networks for transportation mode detection using edge-enabled consumer smartphone sensors. To appear in IEEE Transactions on Consumer Electronics. https://doi.org/10.1109/TCE.2025.3642299
- Ling, Man Ho, Bae, Suk Joo, Jin, Shengxin, & Ng, Hon Keung Tony (2025). An extended Gamma process for accelerated destructive degradation test: Modeling and optimal design. IEEE Transactions on Reliability, vol. 74, no. 3, pp. 4387-4401. https://doi.org/10.1109/TR.2025.3544545
- Huang, Shaoqin, Qin, Hu, & Wang, Yue (2026). Mining novel customer needs for product design from user-generated content through large language model-enabled data augmentation and ensemble learning. To appear in International Journal of Production Research. https://doi.org/10.1080/00207543.2026.2625971
- Zhuang, Yipeng, Wang, Chenlu & Yu, Philip L.H. (2026). Preference modeling with multi-graph graph attention network. Neurocomputing, 660, 131872. https://doi.org/10.1016/j.neucom.2025.131872
- Zhuang, Yipeng, Li, Dong, Yu, Philip L.H., & Li, Wai Keung (2025). On buffered moving average models. Journal of Time Series Analysis, 46, 599-622.
- Zhang, Junyi, & Dassios, Angelos (2025). Posterior sampling from truncated Ferguson-Klass representation of normalised completely random measure mixtures. Bayesian Analysis, 20(3), 795-825. https://doi.org/10.1214/24-BA1421
