DAI, Shan
POSITION/TITLE
Associate Research Fellow (SRIBD)
Adjunct Assistant Professor (CUHKSZ)
RESEARCH FIELD
Time series, Deep learning, Uncertainty decision-making & optimization
shandai@sribd.cn
EDUCATION BACKGROUND
Ph.D. in The Chinese University of Hong Kong
Bachelor’s degree in University of Science and Technology of China
Major Achievements/Honors
Overseas High-Caliber Personnel, Shenzhen
Overseas Research Award in The Chinese University of Hong Kong
Outstanding Graduate in University of Science and Technology of China
BIOGRAPHY
Dr. Dai is currently a Research Scientist (Associate Research Fellow) in Shenzhen Research Institute of Big Data and also serves as an Adjunct Assistant Professor at the School of Data Science, The Chinese University of Hong Kong (Shenzhen). His main research interests include Time series, Deep learning, Uncertainty decision-making & optimization related theory and practice. He received a Bachelor's degree in Science from University of Science and Technology of China, a Ph.D. degree in Statistics from The Chinese University of Hong Kong. He was also recognized as an Overseas High-Caliber Personnel (Shenzhen). He has published several peer-reviewed papers on international journals and conferences, and got several national patents granted and accepted. Dr. Dai is also the Principal Investigator of Guangdong Basic and Applied Basic Research Foundation Project, Shenzhen Excellent Science and Technology Innovation Talents Cultivation Project and so on. Dr. Dai serves as invited reviewers for journals such as Journal of Time Series Analysis, Tsinghua Science and Technology and conferences including NeurIPS, ICML, ICLR, AISTATS and so on.
Selected Papers (*denotes corresponding author, #denotes equal contribution)：
Statistics Theory and Algorithms:
Two-Stage Newsvendor Network Problem: A Data-driven Distributionally Robust Optimization Approach. D Zhang, HH Turan, R Sarker, D Essam, S Dai*, L Zhang. Production and Operations Management, 2026, Accepted.
Mamba Hawkes Process for Event Sequence Modeling. S Dai, Y Shen, Y Liang, C Ma*, A Gao*. Proceedings of the ACM Web Conference(WWW) 2026, 7464-7473.
Understanding and Guiding Layer Placement in Parameter-Efficient Fine-Tuning of Large Language Models. Y Xu*, Y Liang, S Dai*, T Hu, TN Chan, C Ma*. arXiv preprint arXiv:2602.04019.
Large Deviation Algorithms for Thresholding Bandit Problem. M Zhang, G Liu, S Dai*, J Chen, P Fournier-Viger. Big Data Mining and Analytics, 2025, 8 (5), 1189-1209.
Testing of Constant Parameters for Semi-parametric Functional Coefficient Models with Integrated Covariates. S Dai, NH Chan*. Journal of Time Series Analysis, 2023, 44, 474-486.
Statistics and Machine Learning Applications:
Empirical and Machine Learning Forecasting for Offline Retail: Nonlinear Weather Effects and Heterogeneity. M Zhu, C Luo, X Cai, S Dai*, W Xue, L Zhang. Journal of Systems Science and Systems Engineering, 2026, Accepted.
Fire prediction and risk identification with interpretable machine learning. S Dai, J Zhang, Z Huang, S Zeng*. Journal of Forecasting, 2025, 44 (5), 1699-1715.
License recommendation for open source projects in the power industry. X Zhang, H Xu, Q Yu, S Zeng, S Dai, H Yang, S Wu*. Information and Software Technology, 2024, 167, 107391.
