邮箱：pengwu@btbu.edu.cn

地址：北京市房山区北京工商大学良乡主校区东区开云·体育(中国)官方网站209室

个人简介

北京工商大学开云·体育(中国)官方网站副教授，硕士生导师。籍贯江西省新余市。现任中国现场统计研究会因果推断分会理事，北京生物医学统计与数据管理研究会理事。个人主页： https://pengwu.site

研究兴趣

主要研究方向：因果推断、缺失数据、可信人工智能、推荐系统、医疗决策。

主讲课程

本科生课程《数据挖掘》、《数据分析与统计软件》、《随机过程》。

学习经历

2011年9月-2015年7月，江西财经大学统计学专业，经济学学士；

2015年9月-2017年6月，北京师范大学概率论与数理统计专业，理学硕士；

2017年9月-2020年6月，北京师范大学应用统计专业，理学博士。

工作经历

2020年7月-2022年6月，北京大学，北京国际数学研究中心，博士后；

2022年6月至今，北京工商大学开云·体育(中国)官方网站，副教授。

主要科研项目

1. 国家自然科学基金青年基金，基于数据融合的长期因果效应研究，2024年1月-2026年12月，主持；

2. 横向项目，推荐系统中的反事实可解释性与长短期因果效应估计技术研究，2023年8月-2024年8月，主持。

主要学术成果

发表论文30余篇，获专利3项。主要有：

[1] Haoxuan Li, Chunyuan Zheng, Sihao Ding, Peng Wu*, Zhi Geng, Fuli Feng, and Xiangnan He, (2024), Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference for Recommendation. The Twelfth International Conference on Learning Representations. (ICLR 24)

[2] Honglei Zhang, Shuyi Wang, Haoxuan Li, Chunyuan Zheng, Xu Chen, Li Liu, Shanshan Luo*, Peng Wu* (2024), Uncovering the Limitations of Eliminating Selection Bias for Recommendation: Missing Mechanisms, Disentanglement, and Identifiability. (ICDE 24)

[3] Wenjie Hu, Xiao-Hua Zhou, and Peng Wu* (2023), Identification and estimation of treatment effects on long-term outcomes in clinical trials with external observational data. Statistica Sinica

[4] Haoxuan Li, Chunyuan Zheng, Yanghao Xiao, Hao Wang, Fuli Feng, Xiangnan He, Zhi Geng, and Peng Wu* (2023), Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach. Thirty-seventh Conference on Neural Information Processing Systems. (NeurIPS 23)

[5] Haoxuan Li, Chunyuan Zheng, Yixiao Cao, Zhi Geng, Yue Liu*, and Peng Wu* (2023), Trustworthy Policy Learning under the Counterfactual No-Harm Criterion. Fortieth International Conference on Machine Learning. (ICML 23)

[6] Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Wu*, and Peng Cui (2023), Propensity Matters: Measuring and Enhancing Balancing for Recommendation. Fortieth International Conference on Machine Learning. (ICML 23)

[7] Haoxuan Li, Quanyu Dai, Zhenhua Dong, Xiao-Hua Zhou, and Peng Wu* (2023), Multiple Robust Learning for Recommendation. Proceedings of the 37th AAAI Conference on Artificial Intelligence. (AAAI 23, Oral)

[8] Haoxuan Li, Yan Lyu, Chunyuan Zheng, and Peng Wu* (2023), TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations. Proceedings of the 11th International Conference on Learning Representations. (ICLR 23)

[9] Haoxuan Li, Chunyuan Zheng, and Peng Wu* (2023), StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random. Proceedings of the 11th International Conference on Learning Representations. (ICLR 23)

[10] Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, and Peng Wu* (2023), Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations. Proceedings of the ACM Web Conference 2023. (WWW 23, Best Student Paper Runner-up)

[11] Peng Wu, Zhiqiang Tan, Wenjie Hu, and Xiao-Hua Zhou (2022), Model-Assisted Inference for Covariate-Specific Treatment Effects with High-dimensional Data. Statistica Sinica.

[12] Peng Wu, Shasha Han, Xingwei Tong, and Runze Li (2022), Propensity score regression for causal inference with treatment heterogeneity. Statistica Sinica.

[13] Sihao Ding, Peng Wu*, Fuli Feng, Yitong Wang, Xiangnan He, Yong Liao, and Yongdong Zhang (2022), Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. (KDD 22)

[14] Peng Wu, Haoxuan Li, Yuhao Deng, Wenjie Hu, Quanyu Dai, Zhenhua Dong, Jie Sun, Rui Zhang, and Xiao-Hua Zhou (2022), On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges. International Joint Conference on Artificial Intelligence. (IJCAI 22)

[15] Quanyu Dai, Haoxuan Li, Peng Wu*, Zhenhua Dong, Xiao-Hua Zhou*, Rui Zhang, Xiuqiang He, Rui Zhang, and Jie Sun (2022), A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. (KDD 22)

[16] Peng Wu, Xinyi Xu, Xingwei Tong, Qing Jiang, and Bo Lu (2021), Semi-parametric Estimation for Average Causal Effects using Propensity Score based Spline, Journal of statistical planning and inference. 212, 153-168.

[17] Peng Wu, Baosheng Liang, Yifan Xia, and Xingwei Tong (2020), Predicting Disease Risk by Matching Quantile estimation for Censored Data, Mathematical Biosciences and Engineering. 17(5):4544-4562.

[18] Peng Wu, Qirui Hu, Xingwei Tong, and Min Wu (2020), Learning Causal Effect Using Machine Learning with Application to China's Typhoon. Acta Mathematicae Applicatae Sinica, English Series. 36(3): 702-713.