Le OUYANG, Professor

Professor at Shenzhen MSU-BIT University (SMBU).

欧阳乐

深圳北理莫斯科大学工程系教授、博士生导师

He received the PhD degree from Sun Yat-sen University in 2015. He is a Senior Member of the China Computer Federation (CCF) and has received the Guangdong Outstanding Young Scholar, the Shenzhen Excellent Young Scholar, and has been selected for the Guangdong Pearl River Talent Program. He has led three NSFC projects. He has published over 70 SCI papers in top-tier journals such as Nature Biotechnology, IEEE TCYB, Bioinformatics, and Briefings in Bioinformatics. He serves as Associate Editor for IEEE Transactions on Computational Biology and Bioinformatics, and as an Editorial Board Member for Neural Networks, and serves as a reviewer for journals such as Nature Communications, Nucleic Acids Research, Genome Biology, Advanced Science, IEEE TPAMI, and IEEE TNNLS. He is also a program committee member for major international conferences, including AAAI, IJCAI, ICML, NIPS, and ICLR. Additionally, he is a committee member of the Professional Committee of Bioinformatics of the China Computer Federation (CCF), the Professional Committee of Bioinformatics and Artificial Life of the Chinese Association for Artificial Intelligence (CAAI), the Professional Committee of Intelligent Health and Bioinformatics of the Chinese Association of Automation (CAA), and a board member of the Guangdong Bioinformatics Society.

Research interests: Machine learning, Bioinformatics

2015年在中山大学获得博士学位，2013-2014年在新加坡南洋理工大学计算机科学系交流访问，2015-2016年在香港城市大学电子工程系从事博士后研究。主要从事机器学习、数据挖掘和生物信息学等领域的科研和教学工作。CCF高级会员，广东省杰青、深圳市优青获得者，入选广东省珠江人才计划。主持国家自然科学基金3项、广东省自然科学基金3项、市级项目4项，已在 Nature Biotechnology、IEEE TCYB、Bioinformatics、Briefings in Bioinformatics 等国际期刊发表 SCI 论文 70 余篇。担任国际权威期刊IEEE Transactions on Computational Biology and Bioinformatics副主编、Neural Networks编委，以及Nature Communications、Nucleic Acids Research、Genome Biology、Advanced Science、IEEE TPAMI、IEEE TNNLS等重要刊物审稿人，AAAI、IJCAI、ICML、NIPS、ICLR等国际学术会议程序委员会委员。担任中国计算机学会生物信息学专委会委员、中国人工智能学会生物信息学与人工生命专委会委员、中国自动化学会智能健康与生物信息专委会委员、广东省生物信息学会理事。

研究方向：机器学习、生物信息学和健康医疗大数据

研究生招生方向：

博士：计算机科学与技术

硕士：计算机科学与技术、计算机技术、人工智能

Selected Papers

[1] Weiming Yu, Zerun Lin, Miaofang Lan, Le Ou-Yang*, GCLink: a graph contrastive link prediction framework for gene regulatory network inference, Bioinformatics, 41(3): btaf074, 2025.

[2] Zibo Huang, Xinrui Weng, Le Ou-Yang*, GFLearn: Generalized Feature Learning for Drug-Target Binding Affinity Prediction, IEEE Journal of Biomedical and Health Informatics, 2025, in press.

[3] Yujie Chen, Wenhui Wu*, Le Ou-Yang*, Ran Wang, Sam Kwong, GRESS: Grouping Belief-Based Deep Contrastive Subspace Clustering, IEEE Transactions on Cybernetics, 55(1): 148-160, 2025.

[4] Fuqun Chen, Guanhua Zou, Yongxian Wu, Le Ou-Yang*, Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning, Bioinformatics, 40(4): btae169, 2024.

[5] Zerun Lin, Le Ou-Yang*, Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning, Briefings in Bioinformatics, 24(1): bbac586, 2023.

[6] Youlin Zhan, Jiahan Liu, Le Ou-Yang*, scMIC: A Deep Multi-Level Information Fusion Framework for Clustering Single-Cell Multi-Omics Data, IEEE Journal of Biomedical and Health Informatics, 27(12): 6121-6132, 2023.

[7] Wenhui Wu, Yujie Chen, Ran Wang, Le Ou-Yang*, Self-representative kernel concept factorization, Knowledge-Based Systems, 259: 110051, 2023.

[8] Guanhua Zou, Yilong Lin, Tianyang Han, Le Ou-Yang*, DEMOC: a deep embedded multi-omics learning approach for clustering single-cell CITE-seq data, Briefings in Bioinformatics, 23(5): bbac347, 2022.

[9] Le Ou-Yang, Fan Lu, Zi-Chao Zhang, Min Wu, Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey, Briefings in Bioinformatics, 23(1): bbab479, 2022.

[10] Le Ou-Yang, Dehan Cai, Xiao-Fei Zhang, Hong Yan, WDNE: an integrative graphical model for inferring differential networks from multi-platform gene expression data with missing values, Briefings in Bioinformatics, 22(6): bbab086, 2021.

[11] Xiao-Fei Zhang, Le Ou-Yang*, Ting Yan, Xiaohua Tony Hu, Hong Yan, A joint graphical model for inferring gene networks across multiple subpopulations and data types, IEEE Transactions on Cybernetics, 51(2): 1043-1055, 2021.

[12] Le Ou-Yang, Xiao-Fei Zhang, Hong Yan, Sparse regularized low-rank tensor regression with applications in genomic data analysis, Pattern Recognition, 107: 107516, 2020.  

获奖信息：

2011年获中山大学优秀研究生

2013年获博士研究生国家奖学金

2017年获深圳市海外高层次人才（孔雀计划）C类

2018年获南山区“领航人才”C类

2018年获广东省珠江人才计划青年拔尖人才

2022年获腾讯益友奖“优秀班主任”

2022年获广东省大学生创新创业训练计划优秀指导教师

2023年获广东省大学生计算机设计大赛优秀指导教师

指导学生获奖情况：

指导学生获得美国大学生数学建模竞赛一等奖3项

指导学生获得美国大学生数学建模竞赛二等奖5项

指导学生获得中国大学生计算机设计大赛一等奖1项、二等奖4项、三等奖2项

指导学生获得2019年mathorcup高校数学建模挑战赛二等奖1项

指导国家级大学生创新创业训练计划3项

指导省级大学生创新创业训练计划4项

指导学生获得首届“兴智杯”全国人工智能创新应用大赛三等奖1项

指导学生获得首届“兴智杯”全国人工智能创新应用大赛行业赋能专题赛一等奖1项

指导学生获得中国电子学会2023首届大学生算法大赛三等奖2项

研究生招生：

招收有志于从事科学研究的学生（学术型和专业型均可）

要求：

a) 高等数学、线性代数、数值分析、概率论与数理统计等课程基础扎实；

b) 至少精通两门编程语言：Matlab、Python、C++、Java；

c) 英语的听说读写能力强，有较强的英文阅读和写作能力；

d) 对研究方向感兴趣，对科学研究有热情，不怕吃苦、不怕失败、做事认真负责。混学位者请勿联系。

注：从事科学研究并不指毕业后只是在高校和研究所工作，也指愿意毕业后去著名公司从事研发工作或研究院工作、或出国留学继续攻读博士学位等。

本科生招生：

招收有志于继续攻读硕士研究生或有志于在本科/硕士阶段后攻读国外大学硕士/博士学位的1-3年级本科生。

要求：有志于在未来从事机器学习和生物信息学方向学术研究的学生，尤其侧重于健康医疗大数据和机器学习算法研究。要求数学和编程相关课程学业成绩较高。能够把课余的50～70%的时间全部用于科研中，只有集中精力做好一件事才能做好。

修读或自修以下课程：线性代数、概率统计、Matlab/Python、多元统计分析、矩阵论。