BIMSA > Rui Ze Gao
Postdoc Rui Ze Gao

Group: Artificial Intelligence and Machine Learning , Digital Economy

Office: A3-1-304

Email: gaoruize@bimsa.cn

Research Field: Fintech, Quantitative Investment, Business Intelligence Analysis, Data Assets Research

CV

Biography

Ruize Gao is Post-Doctoral Fellow at Beijing Institute of Mathematical Sciences and Applications and Tsinghua University. His research interests include digital economy, data economy and data mining. He has published in leading journals such as Information Sciences, Knowledge-based Systems, Expert Systems with Applications, Technology in Society, International Journal of Accounting Information Systems. He hosts one funding project under the China Postdoctoral Science Foundation.

Education Experience

  • 2017 - 2023      Chongqing University      Management Science and Engineering      Doctor
  • 2013 - 2017      Chongqing University      Information Management and Information Systems      Bachelor

Work Experience

  • 2023 -      Tsinghua University & BIMSA      Postdoc

Publication

  • [1] Shaoze Cui, Ruize Gao, Junwei Kuang, Liang Yang, Huaxin Qiu, Xiaowen Wei, An interpretable imbalance ensemble classification method for readmission risk assessment incorporating multi-view perturbation and SHAP analysis, Decision Support Systems, 190, 114404 (2025)
  • [2] Ruize Gao, Shaoze Cui, Yu Wang, Wei Xu, Predicting financial distress in high-dimensional imbalanced datasets: A multi-heterogeneous self-paced ensemble learning framework, Financial Innovation (2025)
  • [3] Ying Zhou, Zhi Xiao, Ruize Gao, and Chang Wang, Using data-driven methods to detect financial statement fraud in the real scenario, International Journal of Accounting Information Systems, 54(2024), 100693
  • [4] Yuelong Zheng; Bingjie Zhou; Chen Hao; Ruize Gao; Mengya Li, Evolutionary game analysis on the cross-organizational cooperative R&D strategy of general purpose technologies under two-way collaboration, Technology in Society, 76(2024), 102473
  • [5] Ruize Gao, Shaoze Cui, Hongshan Xiao, Weiguo Fan, Hongwu Zhang, and Yu Wang, Integrating the sentiments of multiple news providers for stock market index movement prediction: A deep learning approach based on evidential reasoning rule, Information Sciences, 615(2022), 529-556
  • [6] Ruize Gao, Xin Zhang, Hongwu Zhang, Quanwu Zhao, and Yu Wang, Forecasting the overnight return direction of stock market index combining global market indices: A multiple-branch deep learning approach, Expert Systems With Applications, 194(2022), 116506
  • [7] Xin Zhang, Hongshan Xiao, Ruize Gao, Hongwu Zhang, and Yu Wang, K-nearest neighbors rule combining prototype selection and local feature weighting for classification, Knowledge-Based Systems, 243(2022), 108451

 

Update Time: 2025-03-28 01:57:07