黎隽男，男，博士（后），副教授，硕士生导师，工业数智化测试、监测与AI决策应用推广中心带头人。2022年6月毕业于重庆大学计算机学院，获工学博士学位。2023.10于电子科技大学进行博士后联合培养。2023年获重工青年人才称号。主要研究方向为机器学习中的半监督小样本学习及工业应用研究（如信贷风险预测、金融欺诈检测等）。欢迎对科研有浓厚兴趣，致力于从事科学研究及读博士的同学报考。导师亲和力强、学术氛围自由。
现主持主持研国家自然科学基金青年项目（62306050）、国家自然科学基金面上项目（62172065）、教育部人文社会科学研究青年基金项目（24YJC790018）、重庆市自然科学基金面上项目（62172065）、重庆市教育科学“十四五”规划2024年度一般课题（K24YG2080221）、重庆市教委科学技术研究（KJQN202403206、KJQN202403206）等，主持及参与横向项目3项、累积到账50余万。
担任期刊国际顶级或重要科技的SCI期刊（如ITKDE、PR、KBS、INS、NN、ASOC等）的审稿人或加入其编委，共发表SCI论文30余篇，以第一作者身份在中科院一区发表SCI论文10篇，主要成果如下：
[1] Li Junnan, Zhou M, Zhu Q, Wu Q. A framework based on local cores and synthetic examples generation for self-labeled semi-supervised classification [J]. Pattern Recognition, 2023, 134: 109060. （LCSEG-SSC；中科院一区Top期刊）
[2] Li Junnan, Fu S, Fu W, Wang L, Pan X. An efficient framework based on local multi-representatives and noise-robust synthetic example generation for self-labeled semi-supervised classification [J]. Neural Networks, 2025, 185: 107142. （LMR-NRSEG-SSC；中科院一区Top期刊）
[3] Li Junnan, Zhu Q, Wu Q, Fan Z. A novel oversampling technique for class-imbalanced learning based on SMOTE and natural neighbors [J]. Information Sciences, 2021, 565: 438-455. （NaNSMOTE；中科院一区Top期刊）
[4] Li Junnan, Zhu Q, Wu Q, Cheng D. An effective framework based on local cores for self-labeled semi-supervised classification [J]. Knowledge-Based Systems, 2020, 197 (7): 105804. （LC-SSC；中科院一区Top期刊）
[5] Li Junnan, Zhu Q, Wu Q, Zhang Z, Gong Y, He Z, Zhu F. SMOTE-NaN-DE: Addressing the noisy and borderline examples problem in imbalanced classification by natural neighbors and differential evolution [J]. Knowledge-Based Systems, 2021, 223 (8): 107056. （SMOTE-NaN-DE；中科院一区Top期刊）
[6] Li Junnan, Wu Q. A self-training method based on density peaks and an extended parameter-free local noise filter for k nearest neighbor [J]. Knowledge-Based Systems, 2019, 184 (15): 104895. （STDPNF；中科院一区Top期刊）
[7] Li Junnan, Li T. A sample subspace optimization-based framework for addressing mislabeling in self-labeled semi-supervised classification [J]. Applied Soft Computing, 2023, 146: 110687. （SSO-SLSSC；中科院一区Top期刊）
[8] Li Junnan. Oversampling framework based on sample subspace optimization with accelerated binary particle swarm optimization for imbalanced classification [J]. Applied Soft Computing, 2024, 162: 111708. （OF-SSO-ABPSO；中科院一区Top期刊）
[9] Li Junnan. A self-training method based on fast binary bare-bones particle swarm optimization for semi-supervised classification [J]. Engineering Applications of Artificial Intelligence, 2024, 136, 108546.（ST-F3BPSO；中科院一区Top期刊）
[10] Junnan Li, Lufeng Wang, Shun Fu, Wei Fu, Xin Pan. Self-labeled framework with semi-supervised ball k-means clustering-based synthetic example generation for semi-supervised classification in industrial applications [J]. Engineering Applications of Artificial Intelligence, 2025. （SEGBallKmeans-SSC；中科院 一区 Top 期刊）
联系方式：jameslee@uestc.edu.com
