刘健，男，1988年生，山东临沂人，副教授，博士，硕士研究生导师，中国矿业大学优秀青年骨干教师，中国自动化学会会员，江苏省自动化学会会员。
2018年博士毕业于中国矿业大学，于2019年进入中国矿业大学信控学院从事博士后研究工作，2022年进入中国矿业大学信控学院任副教授，2023年进入台州市肿瘤医院从事在职博士后研究工作。
主要研究方向为：机器学习及其应用，生物信息学，多模态数据分析，强化学习等。
在 IEEE Transactions on Knowledge and Data Engineering、 IEEE/ACM Transactions on Computational Biology and Bioinformatics、Briefings in Bioinformatics、自动化学报、控制理论与应用等期刊发表SCI、中文高水平论文20余篇， 授权发明专利2项，出版学术专著1部。
主持国家自然科学基金1项，江苏省自然科学基金1项，参与国家自然基金面上项目多项，承担横向课题多项。
邮箱：liujiansqjxt@126.com
电话：15905216271
围绕研究领域在 IEEE TKDE、IEEE/ACM TCBB、自动化学报等期刊发表SCI、中文高水平论文10余篇。
[1] Liu Jian and Feng Liming. Diversity evolutionary policy deep reinforcement learning[J]. Computational Intelligence and Neuroscience, 2021, 2021: 53001891. (SCI)
[2] Liu Jian, Ge Shuguang, Cheng Yuhu and Wang Xuesong. Multi-view spectral clustering based on multi-smooth representation fusion for cancer subtype prediction[J]. Frontiers in Genetics, 2021, 12: 718915. (SCI)
[3] Liu Jian, Liu Wenfeng, Cheng Yuhu Ge Shuguang and Wang Xuesong. Similarity network fusion based on random walk and relative entropy for cancer subtype prediction of multigenomic data[J]. Scientific Programming, 2021, 2021: 2292703. (SCI)
[4] Liu Jian, Cheng Yuhu, Wang Xuesong and Ge Shuguang. One-step robust low-rank subspace segmentation for tumor sample clustering[J]. Computational Intelligence and Neuroscience, 2021, 2021: 9990297. (SCI)
[5] Liu Jian, Cheng Yuhu, Wang Xuesong, Cui Xiaoluo, Kong Yi, Du Junping. Low rank subspace clustering via discrete constraint and hypergraph
regularization for tumor molecular pattern discovery[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018, 15(5):
1500-1512. (SCI)
[6] Wang Xuesong, Liu Jian*, Cheng Yuhu, Liu Aiping, Chen Enhong. Dual hypergraph regularized PCA for biclustering of tumor gene expression data[J].
IEEE Transactions on Knowledge and Data Engineering, 2019, 31(12): 2292-2303. (SCI)
[7] Liu Jian, Cheng Yuhu, Wang Xuesong, Zhang Lin, Z.Jane Wang. Cancer characteristic gene selection via sample learning based on deep sparse filtering[J]. Scientific Reports, 2018, 8(1): 8270.(SCI)
[8] Liu Jian, Cheng Yuhu, Wang Xuesong, Zhang Lin, Liu Hui. An optimal mean based block robust feature extraction method to identify colorectal cancer genes with integrated data[J]. Scientific Reports, 2017, 7(1): 8584. (SCI)
[9] Liu Jian, Wang Xuesong, Cheng Yuhu, Zhang Lin. Tumor gene expression data classification via sample expansion-based deep learning[J]. Oncotarget, 2017, 8(65): 109646. (SCI)
[10] Liu Jian, Liu Jinxing, Gao Yinglian, Kong Xiangzhen, Wang Xuesong, Wang Dong. A p-norm robust feature extraction method for identifying differentially expressed genes[J]. PloS One, 2015, 10(7): e0133124. (SCI)
[11] Liu Jian, Cheng Yuhu, Wang Xuesong, Cui Xiaoluo. Supervised penalty matrix decomposition for tumor differentially expressed genes selection[J].
Chinese Journal of Electronics, 2018, 27(4): 845-851.(SCI)
[12] Ge Shuguang, Wang Xuesong, Cheng Yuhu and Liu Jian*. Cancer subtype recognition based on Laplacian rank constrained multiview clustering[J]. Genes, 2021, 12(4): 526. (SCI)
[13] Ge Shuguang, Liu Jian, Cheng Yuhu, Meng Xiaojing, Wang Xuesong. Multi-view spectral clustering with latent representation learning for applications on multi-omics cancer subtyping[J]. Briefings in Bioinformatics, 2022, bbac500. (SCI)
[14] Liu Jian, Cheng Yuhu, Wang Xuesong, Kong Yi. Joint sample expansion and 1D convolutional neural networks for tumor classification[C]. In Proceedings of International Conference on Intelligent Computing, Springer, Cham, 2017, 135-141. (EI)
[15] Liu Jian, Hou Long, and Ge Shuguang. Multi-omics cancer subtype recognition based on multi-kernel partition aligned subspace clustering[C]. In Proceedings of International Conference on Intelligent Computing, Springer, Cham, 2023, 395-404. (EI).
[16] Zhang Jiarui, Li Ruilin, Yu Nannan, Liu Jian, and Kong Y. Zero-shot learning based on weighted reconstruction of hybrid attribute groups[C]. In Proceedings of International Conference on Intelligent Computing, Springer, Cham, 2023, 249-260. (EI)
[17] 刘健, 顾扬, 程玉虎, 王雪松. 基于多智能强化学习的乳腺癌致病基因预测[J]. 自动化学报, 2022, 48(5): 1246-1258.. (高水平中文核心)
[18] 刘健, 赵恒一. 基于自生成专家样本的探索增强算法[J]. 控制理论与应用, 2023, 40(3): 485-492. (高水平中文核心)