对外经济贸易大学信息学院

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汪波

 
基本信息
姓名:汪波(理学博士)(CURRICULUM VITAE) (Google Scholar)
职称:讲师
电子邮箱:wangbo [AT] uibe.edu.cn
办公室:博学楼1409
办公电话:010-64492602

教育背景
♦ 2013年12月于中国科学院大学数学科学学院获理学博士学位,导师:石勇教授 
♦ 2010年6月于北京理工大学理学院数学系获理学硕士学位,导师:蒋立宁教授
♦ 2008年6月于北京理工大学理学院数学系理学学士

工作经历
20168月 – 至今             对外经济贸易大学信息学院,讲师
201811月 – 至今           Texas A&M University, Visiting Scholar, Supervisor: Dr. Xia Hu
20144月 – 20167月   中国科学院大学经济与管理学院,博士后,合作导师:田英杰研究员

研究方向
统计机器学习 Statistical Machine Learning
弱标签学习 Weak-Label Learning
♦ 半监督学习 Semi-supervised Learning
♦ 计算机视觉 Computer Vision
♦ 自动机器学习 AutoML
♦ 基于最优化的数据挖掘 Optimization Based Data Mining
♦ 强化学习 Reinforcement Learning
♦ 概率图模型 Probabilistic Graphical Models

Research Interests
My principal research interests lie in the development of efficient algorithms and statistical models for weakly labeled and semi-supervised learning problems. I work with core machine learning methodologies, including kernel methods, generative models, probabilistic and stochastic modeling, scalable optimization algorithms, and deep learning approaches. I am also interested in developing automatic machine learning methods, as well as optimization-based models to address interdisciplinary problems. For instance, I have conducted research on the multiple criteria decision making in credit scoring and network intrusion detection. In addition, numerous artificial intelligence fields, such as reinforcement learning and probabilistic graphical models, are also favored in my study. 

科研成果  Publications
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期刊文章(Selected Peer-reviewed Journal Articles)
1. Zhang F, Liu J, Wang B, Qi Z, Shi Y. A Fast Algorithm for Multi-Class Learning from Label Proportions. Electronics, 2019, 8(6): 609. (pdf)
2. Shi Y, Liu J, Qi Z, Wang BLearning from Label Proportions on High-dimensional Data. Neural Networks, 2018, 103: 9-18. (pdf)
3. Chen Z, Qi Z, Wang B, Cui L, Meng F, Shi Y. Learning with Label Proportions Based on Nonparallel Support Vector Machines. Knowledge-Based Systems, 2017, 119: 126-141.  (pdf) 
4. Qi Z, Wang B, Meng F, Niu L. Learning with Label Proportions via NPSVM. IEEE Transactions on Cybernetics, 2017, 47(10): 3293-3305. (pdf)
5. Qi Z, Wang B, Tian Y, Zhang P. When Ensemble Learning Meets Deep Learning: a New Deep Support Vector Machine for Classification. Knowledge-Based Systems, 2016, 107: 54-60. (pdf)
6. Qi Z, Tian Y, Niu L, Wang B. Semi-supervised Classification with Privileged Information. International Journal of Machine Learning and Cybernetics, 2015, 6(4): 667-676. (pdf)

7. 汪波, 聂晓伟. 基于多目标数学规划的网络入侵检测方法. 计算机研究与发展, 2015, 52(10): 2239-2246. (pdf)
8. Liu F, Shi Y, Wang B. World Search Engine IQ Test Based on the Internet IQ Evaluation Algorithms. International Journal of Information Technology & Decision Making, 2015, 14(2): 221–237. (pdf)
9. Wang B, Shi Y, Tian Y. A Triple Structure of Rough Sets Based on Selection Function. Annals of Data Science, 2015: 1-15. DOI 10.1007/s40745-015-0053-9. (pdf)
10. Wang B, Shi Y. Error Correction Method in Classification by Using Multiple-Criteria and Multiple-Constraint Levels Linear Programming. International Journal of Computers Communications and Control, 2012, 7: 976-989. (pdf)

会议及论文集文章(Selected Peer-reviewed Conference Papers)
1. Li Y, Wang BA Study on Customer Churn of Commercial Banks Based on Learning from Label Proportions, 2018 IEEE International Conference on Data Mining Workshop (ICDMW), 2018. (pdf)
2. Yuan H, Wang B, Niu L. Kernel Extreme Learning Machine for Learning from Label Proportions2018 International Conference on Computational Science Workshop (ICCSW), 2018. (pdf)
3. Wang B, Shi Y. A Study on Error Correction of Multiple Criteria and Multiple Constraint Levels Linear Programming Based ClassificationIEEE/WIC/ACM International Conference on Web Intelligence, 2017: 984-987. (pdf)
4. Wang B, Chen Z, Qi Z. Linear Twin SVM for Learning from Label Proportions. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2015:56-59. (pdf)
5. Wang B, Shi Y, Yang Z, Ju X. An Algebra Description for Hard Clustering. Data Science, Springer International Publishing, 2015: 62-69. (pdf)
6. Yang Z, Shi Y, Wang BSearch Engine Marketing, Financing Ability and Firm Performance in E-commerce. Procedia Computer Science, 2015, 55: 1106-1112. (pdf)
7. Wang B, Shi Y, Huang W, Liu G. Misclassification Minimization Based on Multiple Criteria Linear Programming. 2014 IEEE International Conference on Data Mining Workshop (ICDMW), 2014: 88-92. (pdf)
8. Liu P, Sun J, Han L, Wang B. Research on the Construction of Macro Assets Price Index Based on Support Vector Machine. International Conference on Computational Science Workshop (ICCSW), 2014, 29: 1801-1815. (pdf)
9. Yang Z, Shi Y, Wang B, Yan H. Website Quality and Profitability Evaluation in Ecommerce Firms Using Two-stage DEA Model. Procedoa Computer Science, 2014, 30: 4-13. (pdf)
10. Wang B, Wang Y. A Multiple-Criteria and Multiple-Constraint Levels Linear Programming Based Error Correction Classification Model. Procedia Computer Science, 2013, 17: 1073-1082. (pdf)
11. Wang F, Suphamitmongkol W, Wang B. Advertisement Click-Through Rate Prediction Using Multiple Criteria Linear Programming Regression Model. Procedia Computer Science, 2013, 17: 803-811. (pdf)
12. Wang Y, Wang B, Zhang X. A New Application of the Support Vector Regression on the Construction of Financial Conditions Index to CPI Prediction. International Conference on Computational Science Workshop (ICCSW), 2012, 9: 1263-1272. (pdf)
13. 汪波, 石勇. 一个基于妥协解的多目标线性规划分类模型. 中国管理学年会, 2011. 

学术服务
♦ 曾经或正在担任如下国际期刊审稿人:
 Annals of Data Sciences (AODS)
 Journal of Computational Science (JOCS)
 IEEE Transactions on Cybernetics (TC)
 Information Sciences (INS)
 International Journal of Computers, Communications & Control (IJCCC)
 International Journal of Information Technology & Decision Making (IJITDM)
 Soft Computing (SOCO)
♦ 曾经或正在担任如下国际会议审稿人:
 IEEE International Conference on Data Mining (ICDM)
 International Conference on Computational Science (ICCS)
 IEEE/WIC/ACM International Conference on Web Intelligence (WIC)
 International Conference on Information Technology and Quantitative Management (ICITQM)
 International Conference on Data Science (ICDS) 

获奖情况
► 2015年7月获国际信息技术与量化管理学会The International Academy of Information Technology and Quantitative Management (IAITQM)杰出贡献奖。

研究项目
科研项目:
1. 国家自然科学基金委青年科学基金项目,61702099,基于PAC理论的标签比例学习算法研究,2018/01-2020/12,主持
2. 对外经济贸易大学信息学院“管理科学与工程学科培育专项项目,面向管理决策的非结构化图像挖掘技术研究,2018/05-2020/04,主持
3. 对外经济贸易大学培育项目,18PY45-61702099,大规模标签比例学习问题研究,2018/03-2019/03,主持
4. 对外经济贸易大学新进教师项目,16QD17,弱标签分类问题研究,2017/01-2018/12,主持
5. 国家自然科学基金委面上项目,11671379,非Lipschitz优化的高效光滑化信赖域方法及应用,2017/01-2020/12,参加
6. 国家自然科学基金委重大研究计划,91546201,面向管理决策的非结构化大数据分析方法与关键技术,2016/01-2019/12,参加
7. 北京市社会科学基金青年项目,17GLC052,大数据质量对北京市政府公共服务满意度提升影响机制研究,2017/09-2019/06,参加
8. 国家自然科学基金委面上项目,61472390,可拓支持向量机理论、方法与应用研究,2015/01-2018/12,参加
9. 国家自然科学基金委重点项目,71331005,大数据环境下的管理决策创新研究,2014/01-2018/12,参加
10. 国家自然科学基金国际(地区)合作与交流项目,71110107026,最优化数据挖掘的商业智能方法以及在金融与银行管理中的应用,2012/01-2016/12,参加
11. 国家自然科学基金创新群体项目,
70921061,数据挖掘与知识管理:理论与应用研究,2010/01-2012/12,参加
商业项目:
1. 资产价格指数构建研究,2011年9月-2013年3月财政部委托中国资产评估协会同中国科学院虚拟经济与数据科学研究中心合作的课题
2. 澳大利亚必和必拓公司国际交流项目:石油勘探中的数据挖掘 (Research Project Between BHP Billiton and Graduate University of Chinese Academy of Sciences: Data Mining for Petroleum Exploration)

培养学生
硕士 MS Students
金融学方向
李岳 20179月-20196月 (中国工商银行总行)
钱亚星 20189月-
产业经济学方向
李海 20189月-
卢凯丽 20189月-

所授课程
计算机应用基础
电子商务与贸易案例分析(留学生)
数据科学导论
数据统计分析
管理信息系统
国际服务外包理论与实务

创建:2017.03.15
最后更新:2019.06.18