任亚洲

任亚洲

个人简历(Resume

任亚洲,讲师,硕士生导师。2009年在华南理工大学获信息与计算科学专业学士学位; 2014年获华南理工大学计算机应用技术专业博士学位。曾于2012年8月至2014年7月在美国乔治梅森大学联合培养,合作导师是Carlotta Domeniconi教授。在KAIS和Neurocomputing等国际知名期刊,以及IJCAI,AAAI,ICDM,SDM,ECML/PKDD和IJCNN等国际知名会议上发表多篇论文;是TKDE,KAIS,Neurocomputing,Computational Intelligence等国际期刊审稿人。2015年1月加入电子科技大学计算机科学与工程学院。主要研究兴趣: 机器学习、模式识别和数据挖掘,特别是聚类分析、半监督学习和自步学习等

谷歌学术: Google Scholar

主要学术成果和学术贡献

书本章节

  1. Yazhou Ren. Big Data Clustering and Its Applications in Regional Science. In Book: Big Data for Regional Science. Edited by Laurie A Schintler and Zhenhua Chen. Routledge, 2018, Chapter 21, pages 257-264.

会议论文

  1. Xuanwu Liu, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Yazhou Ren, Maozu Guo. Ranking-based Deep Cross-modal Hashing. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (CCF A类会议), 2019, Accepted.
  2. Yazhou Ren, Xiaohui Hu, Ke Shi, Guoxian Yu, Dezhong Yao, and Zenglin Xu. Semi-supervised DenPeak Clustering with Pairwise Constraints. In Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence (PRICAI) (CCF C类会议), 2018, pages 837–850.
  3. Yazhou Ren, Xin Yan, Zechuan Hu, and Zenglin Xu. Self-Paced Multi-Task Multi-View Capped-norm Clustering. In Proceedings of the 25th International Conference on Neural Information Processing (ICONIP) (CCF C类会议), 2018.
  4. Yuehui Wang, Maozu Guo, Yazhou Ren, Lianyin Jia, Guoxian Yu. Drug Repositioning based on Individual Bi-random Walks on a Heterogenous Network. ISBRA, 2018, Accepted.
  5. Yazhou Ren, Peng Zhao, Yongpan Sheng, Dezhong Yao, and Zenglin Xu. Robust Softmax Regression for Multi-class Classification with Self-Paced Learning. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) (CCF A类会议), 2017, pages 2641-2647.
  6. Yazhou Ren, Peng Zhao, Zenglin Xu, and Dezhong Yao. Balanced self-paced learning with feature corruption. In Proceedings of the International Joint Conference on Neural Networks (IJCNN) (CCF C类会议), 2017, pages 2064-2071.
  7. Yazhou Ren, Uday Kamath, Carlotta Domeniconi, and Guoji Zhang. Boosted Mean Shift Clustering. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) (CCF B类会议), Nancy, France, September 15-19, 2014, pages 646–661.
  8. Yazhou Ren, Carlotta Domeniconi, Guoji Zhang, and Guoxian Yu. A Weighted Adaptive Mean Shift Clustering Algorithm. In Proceedings of the SIAM International Conference on Data Mining (SDM) (CCF B类会议), Philadelphia, Pennsylvania, April 24 - 26, 2014, pages 794-802.
  9. Yazhou Ren, Carlotta Domeniconi, Guoji Zhang, and Guoxian Yu. Weighted-Object Ensemble Clustering. In Proceedings of the IEEE 13th International Conference on Data Mining (ICDM) (CCF B类会议), Dallas, Texas, December 7 - 10, 2013, pages 627-636.
  10. Yanming Yu, Guo-Xian Yu, Xia Chen, and Yazhou Ren. Semi-supervised Multi-label Linear Discriminant Analysis. In Proceedings of International Conference on Neural Information Processing (ICONIP) (CCF C类会议), 2017, pages 688-698.
  11. Xiaofan Que, Yazhou Ren, Jiayu Zhou, Zenglin Xu. Regularized Multi-source Matrix Factorization for Diagnosis of Alzheimer’s Disease. In Proceedings of International Conference on Neural Information Processing (ICONIP) (CCF C类会议), 2017, pages 463-473.
  12. Bin Liu, Zenglin Xu, Bo Dai, Haoli Bai, Xianghong Fang, Yazhou Ren, Shandian Zhe. Learning from Semantically Dependent Multi-Tasks. In Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN) (CCF C类会议), 2017, pages 3498-3505.
  13. Jiehua Wu, Guoji Zhang, Yazhou Ren, Xiayan Zhang and Guoxian Yu. Exploiting Neighbors’ Latent Correlation for Link Prediction in Complex Network. In Proceedings of 2013 International Conference on Machine Learning and Cybernetics (ICMLC) (EI会议), 2013, pages 1077-1082.

期刊论文

  1. Yazhou Ren, Kangrong Hu, Xinyi Dai, Lili Pan, Steven C.H. Hoi, Zenglin Xu. Semi-supervised Deep Embedded Clustering. Neurocomputing (SCI二区), 2019, 325: 121-130.
  2. Yazhou Ren, Carlotta Domeniconi, Guoji Zhang, and Guoxian Yu. Weighted-object ensemble clustering: methods and analysis. Knowledge and Information Systems (KAIS) (SCI三区,CCF B类期刊), 2017, 51(2):661-689.
  3. Yazhou Ren, Guoji Zhang, Guoxian Yu, and Xuan Li. Local and Global Structure Preserving based Feature Selection. Neurocomputing (SCI二区), 2012, 89:147-157.
  4. Yazhou Ren, Guoji Zhang, and Guoxian Yu. Random Subspace based Semi-Supervised Feature Selection. In Proceedings of 2011 International Conference on Machine Learning and Cybernetics (ICMLC) (EI会议), 2011, pages 113-118.
  5. Shudong Huang, Yazhou Ren, Zenglin Xu. Robust Multi-view Data Clustering with Multi-view Capped-Norm K-means. Neurocomputing (SCI二区), 2018, 311: 197–208.
  6. Jie Liu, Guoxian Yu, Yazhou Ren, Maozu Guo, Jun Wang. TrioMDR: Detecting SNP interactions in trio families with model-based multifactor dimensionality reduction. Genomics (SCI三区), 2018, https://doi.org/10.1016/j.ygeno.2018.07.014
  7. Weiwei Zhang, Bin Ye, Weijiang Liang, and Yazhou Ren (*). Preoperative prognostic nutritional index is a powerful predictor of prognosis in patients with stage III ovarian cancer. Scientific Reports (SCI三区), 2017, 7(1): 1-8.
  8. Weiwei Zhang, Kejun Liu, Weijiang Liang, Bin Ye, and Yazhou Ren (*). Pretreatment C‐reactive protein/albumin ratio is associated with poor survival in patients with stage IB‐IIA cervical cancer. Cancer Medicine (SCI三区), 2018, 7(1): 105-113.
  9. Guoxian Yu, Guangyuan Fu, Chang Lu, Yazhou Ren, and Jun Wang. BRWLDA: bi-random walks for predicting lncRNA-disease associations. Oncotarget (SCI一区), 2017, 8(36): 60429.
  10. Jun Wang, Long Zhang, Lianyin Jia, Yazhou Ren, Guo-Xian Yu. Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences. International Journal of Molecular Sciences (SCI三区), 2017, 18(11):1-17.
  11. Weiwei Zhang, Kejun Liu, Bin Ye, Yazhou Ren, and Weijiang Liang. Clinical and biological effects of tumor‑associated lymphocytes in the presence or absence of chemotherapy for malignant ascites in ovarian cancer patients. Oncology letters (SCI四区), 2017, 14(3): 3379-3386.
  12. Jiehua Wu, Guoji Zhang, and Yazhou Ren. A balanced modularity maximization link prediction model in social networks. Information Processing and Management (IPM) (SCI三区, CCF B类期刊), 2017, 53: 295–307.
  13. Jiehua Wu, Guoji Zhang, Yazhou Ren, Xiayan Zhang, and Qiao Yang. Weighted Local Naive Bayes Link Prediction, Journal of Information Processing Systems (EI期刊), 2017, 13(4): 914-927.
  14. Xiayan Zhang, Guoji Zhang, Xuan Li, Yazhou Ren, and Jiehua Wu. Image encryption using random sequence generated from generalized information domain. Chinese Physics B (SCI三区), 2016, 25(5): 054201.
  15. 余国先,张国基,韦佳,任亚洲. 一种基于多图的集成直推分类方法. 电子与信息学报(EI期刊), 2011, 33(8): 1883-1888.

国际访学和交流

08/2012~07/2014 美国乔治梅森大学联合培养博士生,合作导师:Carlotta Domeniconi教授。

讲授课程

人工智能、
程序设计基础(C/C++)、
计算机视觉

科研项目

  1. 01/2019~12/2021 基于自步学习的多任务聚类算法研究(No. 61806043),国家自然科学基金青年基金,26万,在研,主持
  2. 01/2019~12/2023 新型数据管理系统(No. 61832001),国家自然科学基金重点项目,289万,在研,参与。
  3. 01/2019~12/2022 点过程在用户行为建模中的研究及应用(No. 61872062),国家自然科学基金面上项目,64万,在研,参与。
  4. 01/2017~12/2018 自步学习理论及其在阿兹海默病症诊断中的应用研究(No. ZYGX2016J078),中央高校基本科研业务费,9万,在研,主持
  5. 01/2017~12/2018 针对阿兹海默病症的多任务自步学习算法研究(No. 2016M602674),中国博士后科学基金第60批面上资助,5万,在研,主持
  6. 01/2015~12/2016 基于集成的大规模无参数聚类算法研究(No. ZYGX2015KYQD042),电子科技大学引进骨干教师科研启动基金,13.5万,已结题,主持
  7. 01/2016~12/2019 大规模张量分析中的非参贝叶斯学习技术研究(No. 61572111),国家自然科学基金面上项目,73万,在研,参与。
  8. 01/2016~12/2016 基于移动互联网大数据的内容智能推荐场景和算法研究(No. MCM20150505),教育部-中国移动科研基金项目,100万,已结题,参与。
  9. 01/2015~12/2016 可扩展的贝叶斯学习算法及在大规模社会网络中的应用(No. CASNDST201402),中国科学院网络数据科学与技术重点实验室开放基金,6万,已结题,参与。

教改项目

  1. 01/2019~12/2020 挑战性学习课程“计算机视觉”建设(No. 2018XJYT-ZD37),电子科技大学2018年度本科教学改革“挑战性学习课程建设项目”,校级重点项目,2.5万,主持
  2. 01/2019~12/2020 机器学习前沿(No. 2018XKQY0085),电子科技大学学科前沿课(第三批),参与。
  3. 04/2017~06/2019 教学方法与考核方法改革示范课“人工智能”建设(No. 2017XJYJ-ZD19),电子科技大学教学改革研究项目,参与。

联系方式

E-mail: yazhou.ren@uestc.edu.cn

地址:四川省成都市高新西区西源大道2006号