Professor Zhou has made significant contributions to Artificial Intelligence, particularly in Recommendation Systems, Large Language Models (LLMs), and statistical learning for healthcare data. He and his group members introduced causal inference into recommendation algorithms, proposed causal-based frameworks to improve accuracy, fairness, and interpretability, and explored the integration of statistical reasoning with large-scale language models to enhance robustness and explainability. His work bridges modern AI techniques with statistical theory, promoting the development of interpretable intelligent systems.

Selected Publications:

1. Li X, Zhou XH*. Temporal visiting-monitoring feature interaction learning for modelling structured electronic health records. Knowledge-Based Systems [Internet]. 2025 Jul 23;327:114155

2.  Li HX, Zheng CY, Wang S, Wu K, Wang E, P Wu,  Geng Z, Chen X, Zhou XH*. Relaxing the Accurate Imputation Assumption. Proceedings of the 41st International Conference on Machine Learning, 2024

3.  Li HX, Zheng CY, Wang W,  Wang E, Feng F, and Zhou XH*. 2024. Denoising Recommendation on Data Missing Not at Random. .In Proceedings of August 25–29, 2024 (KDD). ACM, New York, NY, USA

4. Hu T and Zhou XH*. Debiasing the Conversion Rate Prediction Model in the Presence of Delayed Implicit Feedback. Entropy 2024 Sep 15;26(9):792

5. Li HX , Dai Q,  Li Y, Lyu Y,  Dong ZH, Zhou XH, Wu P. Multiple Robust Learning for Recommendation. The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23) 2023

6. Wu P, Li H, Deng Y, Hu W, Dai Q, Dong Z*, Sun J, Zhang R, Zhou XH*. On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges. In International Joint Conferences on Artificial Intelligence. 2022