DAI, Shan
POSITION/TITLE
SRIBD Research Scientist
CUHKSZ Adjunct Assistant Professor
RESEARCH FIELD
Time series, Point Process, Machine 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. Shan Dai currently serves as a Research Scientist at Shenzhen Research Institute of Big Data and 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, 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 statistical journals and machine learning conferences, and got several patents granted and accepted. Dai is currently the Principal Investigator of a Shenzhen Excellent Science and Technology Innovation Talents Cultivation Project (PhD Start-Up). Dai also serves as invited reviewer for Journal of Time Series Analysis、NeurIPS and the special issue editorial board member for Journal of Shenzhen University (Science and Engineering).
Selected Papers (*denotes corresponding author, #denotes equal contribution)：
Statistics Theory and Algorithms Development:
Large Deviation Algorithms for the Thresholding Bandit Problem. Submitted.
Gao, A#, Dai, S#, & Hu, Y. (2024). Mamba Hawkes Process. arXiv preprint arXiv:2407.05302.
Gao, A#, & Dai, S#. (2024). RoTHP: Rotary Position Embedding-based Transformer Hawkes Process. arXiv preprint arXiv: 2405.06985.
Dai, S. & Chan, N.H.* (2023). Testing of Constant Parameters for Semi-Parametric Functional Coefficient Models with Integrated Covariates. J. Time Ser. Anal., 44: 474-486.
Zhang, M., He, Y., Liu, G., & Dai, S.*(2023). Input Uncertainty Quantification Via Simulation Bootstrapping. Proceedings of the 2023 Winter Simulation Conference (WSC) . IEEE.
Statistics Methods and Machine Learning Applications:
Fire Prediction and Risk Identification with Interpretable Machine Learning. Submitted.
Zhang, X., Xu, H., Yu, Q., Zeng, S., Dai, S., Yang, H., & Wu, S.* (2024). License recommendation for open source projects in the power industry. Information and Software Technology, 167, 107391.
