BIMSA > Xing Yan
Associate Professor Xing Yan

Group: Digital Economy

Office: A15-206

Email: yanxing@bimsa.cn

Research Field: AI and Digital Finance

Webpage: https://sites.google.com/view/xingyan

Biography

I am an Associate Professor at the Beijing Institute of Mathematical Sciences and Applications, since 2025. Prior to this role, I was an Assistant Professor at the Institute of Statistics and Big Data, Renmin University of China, and a postdoctoral researcher at the School of Data Science, City University of Hong Kong. My research lies at the intersection of AI and finance/business, focusing on FinTech and Business Analytics through innovative machine learning and data science methodologies. My interests include tail risk management, empirical asset pricing, portfolio optimization, derivatives, consumer credit, and related areas. Recently, I have also developed an interest in out-of-distribution (OOD) generalization and uncertainty quantification in machine learning. I publish in both finance/business and machine learning academic journals and conferences.

Research Interest

  • Machine Learning, FinTech, Business Analytics
  • OOD Generalization, Uncertainty Quantification

Education Experience

  • 2015 - 2019      The Chinese University of Hong Kong      SEEM (Financial Engineering)      Ph.D      (Supervisor: Prof. Qi Wu)
  • 2012 - 2015      Institute of Computing Technology, Chinese Academy of Sciences      Computer Science      Master
  • 2008 - 2012      Nankai University      Pure Mathematics (Shiing-Shen Chern Class)      Bachelor

Work Experience

  • 2025 -      Beijing Institute of Mathematical Sciences and Applications      Associate Professor
  • 2020 - 2025      Institute of Statistics and Big Data, Renmin University of China      Assistant Professor
  • 2019 - 2020      School of Data Science, City University of Hong Kong      Postdoctoral Researcher

Publication

  • [1] Z Xian, X Yan, CH Leung, Q Wu, Risk-Neutral Generative Networks, arXiv, 2405.17770 (2024)
  • [2] Y Liao, Q Wu, X Yan, Invariant Random Forest: Tree-Based Model Solution for OOD Generalization, AAAI Conference on Artificial Intelligence (AAAI), Oral Presentation (2024)
  • [3] N Yang, CH Leung, X Yan, A novel HMM distance measure with state alignment, Pattern Recognition Letters, 186, 314-321 (2024)
  • [4] C Sun, Q Wu, X Yan, Dynamic CVaR Portfolio Construction with Attention-Powered Generative Factor Learning, Journal of Economic Dynamics and Control (JEDC) (2024)
  • [5] X Liu, X Yan, K Zhang, Kernel quantile estimators for nested simulation with application to portfolio value-at-risk measurement, European Journal of Operational Research, 312(3), 1168-1177 (2024)
  • [6] Y Li, CH Leung, X Sun, C Wang, Y Huang, X Yan, Q Wu, D Wang, ..., The Causal Impact of Credit Lines on Spending Distributions, AAAI Conference on Artificial Intelligence (AAAI) (2024)
  • [7] X Yan, Y Su, W Ma, Ensemble Multi-Quantile: Adaptively Flexible Distribution Prediction for Uncertainty Quantification, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2023)
  • [8] W Ma, X Yan, K Zhang, Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning, IEEE Transactions on Neural Networks and Learning Systems (TNNLS) (2023)
  • [9] Y Liao, Q Wu, Z Wu, X Yan, Decorr: Environment partitioning for invariant learning and ood generalization, arXiv, 2211.10054 (2022)
  • [10] Y Huang, CH Leung, Q Wu, X Yan, S Ma, Z Yuan, D Wang, Z Huang, Robust causal learning for the estimation of average treatment effects, 2022 International Joint Conference on Neural Networks (IJCNN), 1-9 (2022)
  • [11] SY Wang, X Yan, BQ Zheng, H Wang, WL Xu, NB Peng, Q Wu, Risk and return prediction for pricing portfolios of non-performing consumer credit, ACM International Conference on AI in Finance (2021)
  • [12] Y Huang, CH Leung, X Yan, Q Wu, N Peng, D Wang, Z Huang, The Causal Learning of Retail Delinquency, AAAI Conference on Artificial Intelligence (AAAI) (2021)
  • [13] X Yan, Q Wu, W Zhang, Cross-sectional Learning of Extremal Dependence among Financial Assets, Neural Information Processing Systems (NeurIPS) (2019)
  • [14] Q Wu, X Yan, Capturing Deep Tail Risk via Sequential Learning of Quantile Dynamics, Journal of Economic Dynamics and Control (JEDC) (2019)
  • [15] X Yan, W Zhang, L Ma, W Liu, Q Wu, Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning, Neural Information Processing Systems (NeurIPS) (2018)
  • [16] X Yan, H Chang, S Shan, X Chen, Modeling video dynamics with deep dynencoder, European Conference on Computer Vision (ECCV) (2014)
  • [17] X Yan, H Chang, X Chen, Temporally multiple dynamic textures synthesis using piecewise linear dynamic systems, IEEE International Conference on Image Processing, 3167-3171 (2013)

 

Update Time: 2025-12-15 09:00:12