教师队伍

吴偶

2019-06-02 19:02

吴偶

  • 职称:

  • 教授

  • 院系:

  • 应用数学中心

  • 电子邮箱:

  • wuou@tju.edu.cn

  • 办公地点:卫津路校区6教


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研究方向

数据挖掘与机器学习  
 

教育背景

·         2008.9-2011.10  中国科学院自动化研究所  计算机应用专  博士 (在职)

·         2003.9-2006.7   中国科学院自动化研究所  模式识别与智能系统  硕士

·         1999.9-2003.7   西安交通大学电气工程学院 电力系统及其自动化 本科

工作经历

²  天津大学  应用数学中心,教授 | 2017.3-至今

²  中国科学院自动化研究所  模式识别国家重点实验室,副研究员 | 2013.11-2017.2

²  中国科学院自动化研究所  模式识别国家重点实验室,助理研究员 | 2007.3-2013.10

教学工作

科研工作

[1] 国家自然科学基金面上项目,排序学习中的对象集结构分析与等序关系输出研究,2017/01-2020/12,批准,主持。

[2] 国家自然科学基金面上项目,网页表观挖掘的关键问题研究,2014/01-2017/12,已结题。

[3] 国家自然科学基金青年项目,动态、分布式网络入侵模式分析,2010/01-2012/12,已结题,主持。

[4] 国家863信息安全重点专项子课题,数据的重复性检测,2012/01-2014/12,已结题,主持。

[5] 天津市自然科学基金重点项目,互联网大规模用户文本内容的语义抽取与识别,2019/09-2022/09,主持。

[6] 之江实验室研究项目,深度元学习的关键问题研究,2019/11-2021/10,主持。

[7] 中国工程院咨询项目课题,天津市算力资源顶层设计研究,2019/11-2020/12,主持.

[8] 企业研发课题,自动机器学习系统研发,2019.8-2020.5,主持.

主要荣誉

[1] 网络信息安全分析与识别的技术、系统及应用,北京市科学技术奖,一等奖, 2012.


[2]一种基于多分类器融合的敏感网页过滤方法及系统,第三届北京市发明专利一等奖,2013


[3] 一种高效的敏感图像过滤方法及其系统,中国专利奖-优秀奖,2013


[4]网页表观的视觉感知与学习,中国人工智能学会优秀博士论文提名,2013

学术兼职。

其它

发表论文

    • 1. Xiaolin Zhou, Ou Wu*, Which Samples Should be Learned First: Eay or Hard? IEEE Transactions on Neural Network and Learning Systems (TNNLS), 2023. CCF B.

    • 2. Mengyang Li, Fengguang Su, Ou Wu*, Ji Zhang, Class-Level Logit Perturbation, IEEE Transactions on Neural Network and Learning Systems (TNNLS), 2023.CCF B.

    • 3. Rujing Yao, Yingchun Ye, Ji Zhang, Shuxiao Li, Ou Wu*, Exploring developments of the AI field from the perspective of methods, datasets, and metrics, Information Processing and Management (IPM), 2023. CCF B.

    • Xiaoling Zhou, Nan Yang, Ou Wu*, Combining Adversaries with Anti-adversaries in Training, AAAI, 2023. CCF A.

    • Ou Wu*, Tao Yang, Mengyang Li, Ming Li, Two-Level LSTM for Sentiment Analysis With Lexicon Embedding and Polar Flipping, IEEE Transactions on Cybernetics (TCYB), 2022. CCF B.

    • Mengyang Li, Fengguang Su, Ou Wu*, Ji Zhang, Logit Perturbation, AAAI, 2022.  CCF A.

    • Xiaoling Zhou, Ou Wu*, Weiyao Zhu, Ziyang Liang, Understanding Difficulty-Based Sample Weighting with a Universal Difficulty Measure. ECML/PKDD, 2022. CCF B.

    • Fengguang Su, Yu Zhu, Ou Wu*, Yingjun Deng, Submodular Meta Data Compiling for Meta Optimization. ECML/PKDD, 2022. CCF B.

    • Rujing  Yao, Linlin Hou, Yingchun Ye, Ou Wu, Ji Zhang, Jian Wu, Method and Dataset  Mining in Scientific Papers, IEEE International Conference on Big Data, 2020.

    • Tao  Yang, Qin Ying, Lei Yang, Ou Wu*,  Aspect-based Sentiment Analysis with New Target Representation and Dependency  Attention, IEEE Transactions on  Affective Computing, Accepted, 2019.

    • Ou Wu*,  Classifier Ensemble by Exploring Side Ordering Information, IEEE Transactions on Knowledge and Data  Engineering (TKDE), 30(11): 2065-2077, 2018.

    • Ou Wu*,  Xue Mao, and Weiming Hu, Iteratively Divide-and-Conquer Learning for Nonlinear  Classification and Ranking, ACM  Transactions on Intelligent Systems and Technology (TIST): 9(2), 1-26,  2018.

    • Ou Wu*,  Mengqiao Han, Screenshot-based color compatibility assessment and transfer for  Web pages. Multimedia Tools  Applications, 77(6): 6671-6698 , 2018.

    • Ou Wu*,  Qiang You, Fen Xia, Lei Ma, and Weiming Hu, Listwise Learning to Rank from  Crowds, ACM Transactions on Knowledge  Discovery from Data (TKDD), Volume 11 Issue 1, Article 4, 39pages, 2016.

    • Yunyan  Duan and Ou Wu* (equal  contribution), Learning with Auxiliary Less-noisy labels, IEEE Transactions on Neural Network and Learning Systems (TNNLS), 28(7): 1716-1721, 2016.

    • Ou Wu*,  Haiqiang Zuo, Bing Li, Weiming Hu, Multi-modal Web Aesthetics Assessment based  on Structural SVM and Multi-task Fusion Learning, IEEE Transactions on Multimedia (TMM), 18(6), pp. 1062-1076, 2016.

    • Ou Wu*,  Qiang You, Xue Mao, Fen Xia, Fei Yuan, Listwise Learning to Rank by Exploring  Structure of Objects, IEEE Transactions  on Knowledge and Data Engineering (TKDE), 28(7), pp. 1934-1939, 2016.

    • Ou Wu*,  Weiming Hu, Lei Shi, Measuring the Visual Complexities of Web Pages, ACM Transactions on the Web (TWEB), 7,  1, Article 1, 34pages, 2013.

    • Ou Wu*,  Weiming Hu, Steve Maybank, Efficient Clustering Aggregation based on Data  Fragments, IEEE Transactions on System,  Man, and Cybernetics, Part B: Cybernetics (TSMC-B), 42(3): 913-926, 2012.

    • Ou Wu*,  Ruiguang Hu, Xue Mao, and Weiming Hu, Quality-based Learning for Web Data  Classification, AAAI Conference on  Artificial Intelligence (AAAI), Oral presentation, pp. 194-200, 2014. 

    • Ou Wu*,  Shuxiao Li, Honghui Dong, Ying Chen, and Weiming Hu, Learning from Multi-User  Multi-Attribute Annotations, SIAM  International Conference on Data Mining (SDM), Oral presentation, pp.  19-27, 2014.

    • Ou Wu*,  Weiming Hu, Jun Gao, Learning to Rank under Multiple Annotators, International Joint Conference on  Artificial Intelligence (IJCAI), pp.1571-1576, 2011.

    • Ou Wu*,  Weiming Hu, Jun Gao, Learning to Predict the Perceived Visual Quality of  Photos, International Conference on  Computer Vision (ICCV), pp.1-8, 2011.

    • Ou Wu*,  Yunfei Chen, Bing Li, Weiming Hu, Evaluating the Visual Quality of Web Pages  Using a Computational Aesthetic Approach, ACM  International Conference on Web Search and Data Mining (WSDM), pp. 337-346,  2011.

    • Ou Wu*,  Jun Gao, Weiming Hu, Bing Li and Mingliang Zhu, Identifying Multi-instance  Outliers, SIAM International Conference  on Data Mining (SDM), Oral presentation, pp. 430-441, 2010.



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