Yiyan Huang (黄一晏)
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Email: huangyiyan@gbu.edu.cn Location: Great Bay University, Songshan Lake, Dongguan, China Welcome to my homepage! I am a tenure-track Assistant Professor (independent PI) of computer science at Great Bay University (大湾区大学) from 2025 Spring. In 2024, I was a Postdoctoral researcher at the Department of Applied Mathematics, Hong Kong Polytechnic University (PolyU), hosted by Prof. Min Dai. In 2023, I obtained my Ph.D. degree in Data Science from the School of Data Science at City University of Hong Kong (CityU), supervised by Prof. Qi Wu. In 2023, I was also a visiting research scholar at the Department of Economics, Brown University, hosted by Prof. Toru Kitagawa. In 2019, I obtained my Bachelor's degree in Finance from the Department of Statistics and Finance at the University of Science and Technology of China (USTC).
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- Siyi Wang, Yiyan Huang, Cheuk Hang Leung, Chaoqun Wang, Qi Wu. A Two-stage Disentangled and Balanced Representation Learning Method for Counterfactual Regression. Information Sciences, 2026.
- Cheuk Hang Leung, Yiyan Huang (co-first author) , Yijun Li, Qi Wu. Distributionally Robust Off-Policy Evaluation and Learning with Continuous Treatments. The 39th AAAI Conference on Artificial Intelligence (AAAI-2025).
- Yiyan Huang, Cheuk Hang Leung, Siyi Wang, Yijun Li, Qi Wu. Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators. The 38th Annual Conference on Neural Information Processing Systems (NeurIPS-2024).
- Yiyan Huang, Siyi Wang, Cheuk Hang Leung, Qi Wu, Dongdong Wang, Zhixiang Huang. DIGNet: Learning Decomposed Patterns in Representation Balancing for Treatment Effect Estimation. Transactions on Machine Learning Research (TMLR, 2024).
- Yijun Li, Cheuk Hang Leung, Xiangqian Sun, Chaoqun Wang, Yiyan Huang, Xing Yan, Qi Wu, Dongdong Wang, Zhixiang Huang. The Causal Impact of Credit Lines on Spending Distributions. The 38th AAAI Conference on Artificial Intelligence (AAAI-2024).
- Shumin Ma, Zhiri Yuan, Qi Wu, Yiyan Huang, Xixu Hu, Cheuk Hang Leung, Dongdong Wang, Zhixiang Huang. Deep into The Domain Shift: Transfer Learning through Dependence Regularization. IEEE Transactions on Neural Networks and Learning Systems (TNNLS, 2023).
- Yiyan Huang, Cheuk Hang Leung, Shumin Ma, Zhiri Yuan, Qi Wu, Siyi Wang, Dongdong Wang, Zhixiang Huang. Towards Balanced Representation Learning for Credit Policy Evaluation. The 26th International Conference on Artificial Intelligence and Statistics (AISTATS-2023).
- Zhiri Yuan, Xixu Hu, Qi Wu, Shumin Ma, Cheuk Hang Leung, Xin Shen, Yiyan Huang. A Unified Domain Adaptation Framework with Distinctive Divergence Analysis. Transactions on Machine Learning Research (TMLR, 2022).
- Yiyan Huang, Cheuk Hang Leung, Shumin Ma, Qi Wu, Dongdong Wang, Zhixiang Huang. Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Information. The 19th Pacific Rim International Conference on Artificial Intelligence (PRICAI-2022).
- Yiyan Huang, Cheuk Hang Leung, Xing Yan, Qi Wu, Shumin Ma, Zhiri Yuan, Dongdong Wang, Zhixiang Huang. Robust Causal Learning for the Estimation of Average Treatment Effects. The 2022 International Joint Conference on Neural Networks (IJCNN-2022, oral).
- Yiyan Huang, Qi Wu, Dongdong Wang, Zhixiang Huang. Interpretable Causal Inference via Causal Graphs. 11th World Congress of the Bachelier Finance Society (abstract oral presentation at BFS-2022).
- Yiyan Huang, Cheuk Hang Leung, Xing Yan, Qi Wu, Nanbo Peng, Dongdong Wang, Zhixiang Huang. The Causal Learning of Retail Delinquency. The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-2021).
- Yiyan Huang, Cheuk Hang Leung, Xing Yan, Qi Wu. Robust Causal Machine Learning of Treatment Effects. Journal of Business & Economic Statistics (JBES, Under review, FT-50, ABS-4).
- Cheuk Hang Leung, Yiyan Huang (co-first author), Yijun Li, Qi Wu. Double Machine Learning for Time-Varying Continuous Treatment Effects. Journal of Business & Economic Statistics (JBES, Under review, FT-50, ABS-4).
📣 Prospective opportunities 📣:
I am looking for self-motivated Visiting students (访问学生), RAs (研究助理), Masters (湾大-南科大联培硕士生), and PhDs (湾大-哈工深联培博士生) to join my research team. I also have several openings for Researcher (特任研究员) and Postdoctoral (博士后) positions, with the postdoctoral certificate awarded by THU or USTC（博后出站颁发清华大学博后证书或中国科学技术大学博后证书）. The salary of a postdoc ranges from 400k-450k CNY/year, and details can be found via GBU Recruitment Page.
Please email me your CV if you are interested in research topics related to Causal Inference and Machine Learning.
Research Interests
My primary research focuses on Causal Inference and Data Science, with a multidisciplinary approach integrating Statistics, Machine Learning, and Operations Research. In particular, most of my work focuses on causal effect estimation and data-driven decision making in areas such as Management Science, Digital Marketing, Healthcare, etc.
Currently, our group is focusing on three research lines (Collaborations are always welcome!):
⭐ 1. [Theoretical] Data-driven causal decision making: Studying the contextual and non-contextual bandit problem in different settings, in order to solve the resource allocation problem in Management Science and Operations Research.⭐ 2. [Theoretical and Experimental] Causal4ML and ML4causal: Bridging the gap between causal inference and machine learning, aiming to address trustworthiness challenges such as robust learning, distribution shifts, and uncertainty.
⭐ 3. [Experimental] Reliable/Trustworthy LLM: Using causal inference and machine learning methods to build more reliable/trustworthy Large Language Models.
Publications
Working Papers
Others
Internship at JD Digital Technology-Risk Management Center-Intelligent Model Department.
Reviewers for IJCAI2021, AISTATS2022, ICML2022, Neurips2022, ICML2023, Neurips2023, ICML2024, Neurips2024, AAAI2025, AISTATS2025, ICLR2025, ICML2025, Neurips2025, ICLR2026, ICML2026, Transactions on Machine Learning Research, etc.