DAI, Shan
POSITION/TITLE
Research Scientist
CUHKSZ Adjunct Assistant Professor
RESEARCH FIELD
Spatio-Temporal data, Point Process, Statistical Machine Learning & Deep Learning
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 spatio-temporal data, point process, statistical machine learning and deep learning 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 currently the Principal Investigator of a Shenzhen Excellent Science and Technology Innovation Talents Cultivation Project. Dai also 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:
Testing of Constant Parameters for Semi-parametric Functional Coefficient Models with Integrated Covariates. Dai, S., N. H. Chan.*(2023). Journal of Time Series Analysis, 44, 474-486.
RoTHP: Rotary Position Embedding-based Transformer Hawkes Process. Dai, S.*#, Gao, A.#, Li, Z., Du, Y. (2024). Big Data Mining and Analytics, Accepted.
Input Uncertainty Quantification Via Simulation Bootstrapping. Zhang, M., He, Y., Liu, G., Dai, S.*(2023). Proceedings of the Winter Simulation Conference (pp. 3693-3704) , IEEE.
Large Deviation Algorithms for the Thresholding Bandit Problem. Zhang, M., Liu, G., Dai, S.*, Chen, J., Fournier-Viger, P. (2025). Big Data Mining and Analytics, Accepted.
Statistics and Machine Learning Applications:
Fire Prediction and Risk Identification with Interpretable Machine Learning. Dai, S., Zhang, J., Huang Z., Q., Zeng, S.*(2025). Journal of Forecasting, Accepted.
Effective Job-market Mobility Prediction with Attentive Heterogeneous Knowledge Learning and Synergy. Lin, S., Zhang Z., Chen Y.*, Ma C.*, Fang Y., Dai, S., Lu, G. (2024). ACM CIKM, pp. 3897-3901.
License Recommendation for Open Source Projects in the Power Industry. Zhang, X., Xu, H., Yu, Q., Zeng, S., Dai, S., Yang, H., Wu, S.* (2024). Information and Software Technology, 167:107391.
