Yiyan Huang (黄一晏)

Email: huangyiyan@gbu.edu.cn

Location: Great Bay University, Songshan Lake, Dongguan, China

Welcome to my homepage! I am Yiyan Huang, a tenure-track Assistant Professor in the School of Computing and Information Technology at Great Bay University, Guangdong, China. My main research focus is Causal Inference and Data Science, at the intersection of Stats&Econ, Operations Research, and Machine Learning.

Academic Positions

2025 – Present: Tenure-track Assistant Professor (Independent PI), School of Computing and Information Technology, Great Bay University.
2024: Postdoctoral Fellow, Department of Applied Mathematics, Hong Kong Polytechnic University (PolyU). Hosted by Prof. Min Dai.
2023: Visiting Research Scholar, Department of Economics, Brown University. Hosted by Prof. Toru Kitagawa.

Education Background

2023: Ph.D., School of Data Science, City University of Hong Kong (CityU). Supervised by Prof. Qi Wu.
2019: Bachelor, Department of Statistics and Finance, University of Science and Technology of China (USTC).

📣 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、湾大-深圳大学联培硕士生), and PhDs (湾大-哈工深联培博士生) to join my research team. I also have several openings for Postdoctoral (博士后) positions, with the postdoctoral certificate awarded by THU or USTC（博后出站颁发清华大学/中国科学技术大学博后联合培养证书）. The salary of a postdoc ranges from 350k-400k 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, Econometrics, Operations Research, and Machine Learning. I'm also interested in applying causal inference and data science methods for solving real-world problems in fields 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 (* indicates corrsponding author)

Yiyan Huang*, Cheuk Hang Leung, Qi Wu, Zhiheng Zhang. Wasserstein Policy Learning for Distributional Outcomes. The 39th Annual Conference on Learning Theory (COLT 2026).
Yuxuan Yang, Dugang Liu*, Yiyan Huang*. Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness. 32nd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2026).
Jiacan Gao, Xinyan Su, Mingyuan Ma, Yiyan Huang, Xiao Xu, Xinrui Wan, Tianqi Gu, Enyun Yu, Jiecheng Guo, Zhiheng Zhang*. Budgeted Active Experimentation for Treatment Effect Estimation from Observational and Randomized Data. 43rd International Conference on Machine Learning (ICML-2026).
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, 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, 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).

Working Papers

Some JOURNAL papers are in progress, e.g., IEEE TPAMI (Major revision), JMLR (Under review), Management Science (Under review, UTD-24), Informs Journal on Computing (Under review, UTD-24).

Others

Internship at JD Digital Technology-Risk Management Center-Intelligent Model Department.

Reviewers for COLT、Neurips、ICML、ICLR、AISTATS、JMLR、TPAMI、Neural Networks、Journal of Biomedical Informatics, etc.