Le OUYANG, Professor

Professor at Shenzhen MSU-BIT University (SMBU).

歐陽樂

深圳北理莫斯科大學工程系教授、博士生導師

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年在中山大學獲得博士學位，2013-2014年在新加坡南洋理工大學計算機科學系交流訪問，2015-2016年在香港城市大學電子工程系從事博士后研究。主要從事機器學習、數據挖掘和生物信息學等領域的科研和教學工作。CCF高級會員，廣東省杰青、深圳市優青獲得者，入選廣東省珠江人才計劃。主持國家自然科學基金3項、廣東省自然科學基金3項、市級項目4項，已在 Nature Biotechnology、IEEE TCYB、Bioinformatics、Briefings in Bioinformatics 等國際期刊發表 SCI 論文 70 余篇。擔任國際權威期刊IEEE Transactions on Computational Biology and Bioinformatics副主編、Neural Networks編委，以及Nature Communications、Nucleic Acids Research、Genome Biology、Advanced Science、IEEE TPAMI、IEEE TNNLS等重要刊物審稿人，AAAI、IJCAI、ICML、NIPS、ICLR等國際學術會議程序委員會委員。擔任中國計算機學會生物信息學專委會委員、中國人工智能學會生物信息學與人工生命專委會委員、中國自動化學會智能健康與生物信息專委會委員、廣東省生物信息學會理事。

研究方向：機器學習、生物信息學和健康醫療大數據

研究生招生方向：

博士：計算機科學與技術

碩士：計算機科學與技術、計算機技術、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.  

獲獎信息：

2011年獲中山大學優秀研究生

2013年獲博士研究生國家獎學金

2017年獲深圳市海外高層次人才（孔雀計劃）C類

2018年獲南山區“領航人才”C類

2018年獲廣東省珠江人才計劃青年拔尖人才

2022年獲騰訊益友獎“優秀班主任”

2022年獲廣東省大學生創新創業訓練計劃優秀指導教師

2023年獲廣東省大學生計算機設計大賽優秀指導教師

指導學生獲獎情況：

指導學生獲得美國大學生數學建模競賽一等獎3項

指導學生獲得美國大學生數學建模競賽二等獎5項

指導學生獲得中國大學生計算機設計大賽一等獎1項、二等獎4項、三等獎2項

指導學生獲得2019年mathorcup高校數學建模挑戰賽二等獎1項

指導國家級大學生創新創業訓練計劃3項

指導省級大學生創新創業訓練計劃4項

指導學生獲得首屆“興智杯”全國人工智能創新應用大賽三等獎1項

指導學生獲得首屆“興智杯”全國人工智能創新應用大賽行業賦能專題賽一等獎1項

指導學生獲得中國電子學會2023首屆大學生算法大賽三等獎2項

研究生招生：

招收有志于從事科學研究的學生（學術型和專業型均可）

要求：

a) 高等數學、線性代數、數值分析、概率論與數理統計等課程基礎扎實；

b) 至少精通兩門編程語言：Matlab、Python、C++、Java；

c) 英語的聽說讀寫能力強，有較強的英文閱讀和寫作能力；

d) 對研究方向感興趣，對科學研究有熱情，不怕吃苦、不怕失敗、做事認真負責。混學位者請勿聯系。

注：從事科學研究并不指畢業后只是在高校和研究所工作，也指愿意畢業后去著名公司從事研發工作或研究院工作、或出國留學繼續攻讀博士學位等。

本科生招生：

招收有志于繼續攻讀碩士研究生或有志于在本科/碩士階段后攻讀國外大學碩士/博士學位的1-3年級本科生。

要求：有志于在未來從事機器學習和生物信息學方向學術研究的學生，尤其側重于健康醫療大數據和機器學習算法研究。要求數學和編程相關課程學業成績較高。能夠把課余的50～70%的時間全部用于科研中，只有集中精力做好一件事才能做好。

修讀或自修以下課程：線性代數、概率統計、Matlab/Python、多元統計分析、矩陣論。