BIMSA > Xing Yan
Associate Professor Xing Yan

Xing Yan

Associate Professor
Affiliation: BIMSA
Research Field: AI and Digital Finance
Office: A15-206
Email: yanxing@bimsa.cn
Webpage: https://scholar.google.com/citations?user=9d2JaVMAAAAJ&hl=en

Biography

I have been an Associate Professor at BIMSA since 2025. Prior to this role, I was an Assistant Professor at the Institute of Statistics and Big Data, Renmin University of China. 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

Publications

  • [1] Z Zhu, Y Huang, X Yan, Active Domain Adaptation Under Concept Shift, IEEE Transactions on Pattern Analysis and Machine Intelligence (2026)
  • [2] X Yan, G Zhang, T Zhao, Neural Importance Sampling for Option Pricing with Normalizing Flows, The Review of Mathematical Economics, 1(1), 101-128 (2026)
  • [3] Z Zhang, K Zhang, X Yan, S Yang, Y Zhang, Big Data in Economics and Management, Springer Nature (2026)
  • [4] Z Xian, X Yan, CH Leung, Q Wu, Risk-Neutral Generative Networks, Quantitative Finance (2026)
  • [5] Y Liao, Q Wu, Y Wu, X Yan, Decorr: Environment partitioning for invariant learning and ood generalization, Neural Networks (2026)
  • [6] C Sun, Q Wu, X Yan, Dynamic CVaR Portfolio Construction with Attention-Powered Generative Factor Learning, Journal of Economic Dynamics and Control (JEDC) (2024)
  • [7] X Yan, Y Zhao, Q Wu, W Ma, Parsimonious Generative Machine Learning for Non-Gaussian Tail Modeling, arXiv, 2402.14368 (2024)
  • [8] W Ma, Q Wu, X Yan, Deep Learning of Conditional Volatility and Negative Risk-Return Relation, SSRN (2024)
  • [9] 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)
  • [10] N Yang, CH Leung, X Yan, A novel HMM distance measure with state alignment, Pattern Recognition Letters, 186, 314-321 (2024)
  • [11] 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)
  • [12] 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)
  • [13] 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)
  • [14] 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)
  • [15] 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)
  • [16] 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)
  • [17] 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)
  • [18] X Yan, Parsimonious Learning of Tail Dynamics, PQDT-Global (2019)
  • [19] X Yan, Q Wu, W Zhang, Cross-sectional Learning of Extremal Dependence among Financial Assets, Neural Information Processing Systems (NeurIPS) (2019)
  • [20] Q Wu, X Yan, Capturing Deep Tail Risk via Sequential Learning of Quantile Dynamics, Journal of Economic Dynamics and Control (JEDC) (2019)
  • [21] 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)
  • [22] X Yan, H Chang, S Shan, X Chen, Modeling video dynamics with deep dynencoder, European Conference on Computer Vision (ECCV) (2014)
  • [23] 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: 2026-09-08 19:48:20