朱晓燕，博士，计算机科学与技术学院副教授、博士生导师。

朱晓燕博士一直从事机器学习和数据挖掘方法的研究和实践；主持国家自然科学基金重大研究计划培育项目、青年项目，主持和参与国家自然科学基金重大项目、面上项目、863高技术研究发展计划等项目；在AAAI、Information Sciences、ACM Transactions on Knowledge Discovery from Data、Knowledge Based Systems等知名期刊和会议发表学术论文近50篇；主讲本科生专业核心课《算法分析与设计》、研究生学位课《机器学习与数据挖掘》，主持、参与《数据结构与算法》课程改革等多项教改项目。

1.基于大学生学习生活行为大数据的学生群体画像研究及其应用，横向课题，主持，进行中

2.基于深度学习的个人信息智能识别方法研究，横向课题，主持，进行中

3.肠道微生态的健康模型研究及其应用，横向课题，主持，进行中

4.基于医疗大数据分析的临床决策支持算法评估、推荐与优化，国家自然科学基金重大研究计划培育项目，主持，已结题

5.软件更改缺陷实时预测方法研究，国家自然科学基金青年科学基金，主持，已结题

6.基于数据挖掘的软件过程模型推荐方法研究，武汉大学国家重点实验室开放课题，主持，已结题

1. Xiaoyan Zhu, Jiaxuan Li, Jingtao Ren, Jiayin Wang, Guangtao Wang, Dynamic ensemble learning for multi-label classification, Information Sciences, 623(2023): 94-111, 2023.

2. Jiaxuan Li, Xiaoyan Zhu, Jiaoyin Wang, AdaBoost.C2: Boosting Classifiers Chains for Multi-label Classification, AAAI, 2023.

3. Xiaoyan Zhu, Nan Li, Yong Wang, Software change-proneness prediction based on deep learning, Journal of software-evolution and process, 34(4): e2434, 2022.

4. Xiaoyan Zhu, Chenzhen Ying, Jiayin Wang, Jiaxuan Li, Xin Lai, Guangtao Wang, Ensemble of ML-KNN for classification algorithm recommendation, Knowledge-based Systems, 221: 106993, 2021.

5. Xiaoyan Zhu, Yingbin Li, Jiayin Wang, Tian Zheng, Jingwen Fu. Automatic recommendation of a distance measure for clustering algorithms, ACM Transactions on Knowledge Discovery from Data, 15(1): 7, 2021.

6. Xiaoyan Zhu, Chenyu Yan, James E. Whitehead, Binbin Niu, Long Pan, Just-in-time defect prediction for software hunks, Software Practice and Experience, 52(1): 130-153, 2021.

7. Xiaoyan Zhu, Yu Wang, Yingbin Li, Yonghui Tan, Guangtao Wang, Qinbao Song, A new unsupervised feature selection algorithm using similarity-based feature clustering, Computational Intelligence, 95: 103855, 2019.

8. Xiaoyan Zhu, Xiaomei Yang, Chenzhen Ying, Guangtao Wang, A new classification algorithm recommendation method based on link prediction, Knowledge Based Systems, 159: 171-185, 2018.

9. Xiaoyan Zhu, Binbin Niu, E. James Whitehead Jr., Zhongbin Sun, An empirical study of software change classification with imbalance data-handling methods, Software Practice and Experience, 48(11): 1968-1999, 2018.

10. Xiaoyan Zhu, Yueyang He, Long Cheng, Xiaolin Jia, Leizhu, Software change-proneness prediction through combination of bagging and resampling methods, Journal of Software: Evolution and Process, 30(12): e2111, 2018.