刘学军 - 南京航空航天大学 - 计算机科学与技术学院/人工智能学院

个人简介

刘学军，教授，博士生导师。1999年和2002年分别本科和硕士毕业于南京航空航天大学计算机科学与技术学院，2006年博士毕业于英国曼彻斯特大学计算机学院，2006年9月至今在南京航空航天大学任教。现任中国计算机学会生物信息学专委会委员，江苏省人工智能学会模式识别专委会委员，江苏省生物信息学专委会委员，数据采集与处理编委。研究领域为人工智能及其应用研究，主要研究机器学习及在多学科中的应用，如生物信息学、航空飞行器优化设计、航空流场数据建模等，与多个航空航天主流单位具有项目合作关系。合作主持“空气动力学与人工智能”跨学科项目十余项，已发表学术论文60余篇，团队的学科交叉工作近两年做邀请报告三十余次。团队评校2019-2020年度“人工智能与空气动力学科交叉五好导学团队”，获2020年第五届江苏省科协青年会员创新创业大赛信息技术领域创新组唯一的一等奖。教育经历 2003.10 -- 2006.11英国曼彻斯特大学计算机科学博士研究生毕业哲学博士学位 1999.9 -- 2002.3南京航空航天大学计算机软件及理论硕士研究生毕业工学硕士学位 1995.9 -- 1999.6南京航空航天大学计算机及应用大学本科毕业工学学士学位工作经历 2007.5 -- 至今南京航空航天大学计算机科学与技术学院科研项目 [1]基于数据挖掘的高速流场复杂流动现象的因果联系 [2]目标体系分析评估系统 [3]飞行冲突检测模型库和战术级任务线性规划分配分析模型软件 [4]基于概率模型的聚类分析研究 [5]高通量RNA-Seq测序数据的基因表达水平建模研究 [6]基于机器学习方法的气动、隐身一体优化技术研究 [7]下一代测序技术下基因表达数据分析 [8]在基因芯片数据中寻找差异基因的概率方法研究 [9]基于概率模型的基因芯片数据分析 [10]基于概率模型的聚类分析研究 [11]基于概率模型的聚类分析研究

近期论文

查看导师新发文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

1. Zijing Liu, Wei An, Xiyao Qu, Xuejun Liu, and Hongqiang Lyu, "Portfolio-Based Bayesian Optimization for Airfoil Design, " AIAA Journal, Vol. 59, No. 6 (2021), pp. 1975-1989 doi: doi/abs/10.2514/1.J059812. 2. Wu, Tingfan; Liu Xuejun*; An, Wei; Huang Zenghui; Lyu, Hongqiang. A Mesh Optimization Method Using Machine Learning Technique and Variational Mesh Adaptation, Chinese Journal of Aeronautics, 2021, In press, https://doi.org/10.1016/j.cja.2021.05.018. 3. H. Wu, X .Liu*, W. An, H. Lyu. A generative deep learning framework for airfoil flow field prediction with sparse data, Chinese Journal of Aeronautics, 2021, In press, https://doi.org/10.1016/j.cja.2021.02.012. 4. 胡伟杰, 黄增辉, 刘学军*,吕宏强, 基于自动核构造高斯过程的导弹气动性能评估, 航空学报, 2021, 42(4):524093. 5. Li, J., Liu, X.* and Zhang, D., Detecting differential transcript usage across multiple conditions for RNA-seq data based on the smoothed LDA model, Frontiers of Computer Science , 2021 15: 153319. 6. Wu, H., Liu, X.*, An, W., Chen, S. and Lyu, H., A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils, Computers and Fluids, 2020 198: 104393. 7. Yu, X. and Liu, X.*, Mapping RNA-seq reads to transcriptomes efficiently based on learning to hash method, Computers in Biology and Medicine, 2020 116: 103539. 8. Sun, L, An, W., Liu, X.* and Lyu, H., On developing data-driven turbulence model for DG solution of RANS, Chinese Journal of Aeronautics, 2019 32:1869-1884. 9. 石险峰, 刘学军, 张礼. PUseqClust: 一种RNA-seq数据聚类分析方法, 软件学报, 2019, 30: 2857-2868. 10. Liu, Z., Liu, X.* and Cai, X. A new hybrid aerodynamic optimization framework based on differential evolution and invasive weed optimization, Chinese Journal of Aeronautics, 2018 31:1437-1448. 11. 王凯莉, 张礼, 刘学军. 融合多平台表达数据的转录组差异表达分析, 计算机学报, 2018, 41: 1195-1210. 12. Zhang, L. and Liu, X., A Comprehensive Review on RNA-seq Data Analysis, Transactions of Nanjing University of Aeronautics and Astronautics, 2016 33:339-361. 13. Zhang, L. and Liu, X., PBSeq: Modeling based-level bias to estimate gene and isoform expression for RNA-seq data, International Journal of Machine Learning and Cybernetics, 2016 doi:10.1007/s13042-016-0497-z. 14. Zhang, L. and Liu, X., A structured sparse regression method for estimating isoform expression level from multi-sample RNA-seq data, Genetics and Molecular Research, 2016 15:gmr7670. 15. Zhang, Li,Liu, Xuejun(*),Chen, Songcan, Detecting differential expression from RNA-seq data with expression measurement uncertainty, Frontiers of Computer Science in China,2015,9(4) 16. Liu, Xuejun,Zhang, Li,Chen, Songcan,Modeling exon-specific bias distribution improves the analysis of RNA-seq data,PLos One,2015,10(10) 17. Liu, Xuejun,Shi, Xinxin,Chen, Chunlin,Zhang, Li,ImprovingRNA-Seq expression estimation by modeling isoform- and exon-specific readsequencing rate,BMC Bioinformatics,2015,16 18. Liu, Xuejun,Zhu, Qinglei,Lu, Hongqiang,Modeling multi-responsesurfaces for airfoil design with multiple output Gaussian process regression,Journal of Aircraft,2014,51(3):740-747 19. Liu, Xuejun,Gao, Zhenzhu,Zhang, Li,Rattray, Magnus,puma 3.0: improved uncertainty propagation methods for gene and transcript expression analysis,BMC Bioinformatics,2013,14 20. Liu, Xuejun,Rattray, Magnus,Including probe-level measurement error in robust mixture clustering of replicated microarray gene expression,Statistical Applications in Genetics and Molecular Biology,2010,9(1):1-24 21. Liu, Xuejun,Lin, Kevin K.,Andersen, Bogi,Rattray, Magnus,Includin g probe-level uncertainty in model-based gene expression clustering,BMC Bioinformatics,2007,8:1-19 22. Liu, Xuejun,Marta Milo,Neil Lawrence,Magnus Rattray,Probe-level measurement error improves accuracy in detecting differential gene expression,Bioinformatics,2006,22:2107-2113 23. Liu, Xuejun,Marta Milo,Neil Lawrence,Magnus Rattray,A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips,Bioinformatics,2005,21:3637-3644 24. Pearson, Richard D.,Liu, Xuejun,Sanguinetti, Guido,Milo, Marta,Lawrence, Neil D.,Rattray, Magnus,puma: a Bioconductor package for propagating uncertainty in microarray analysis,BMC Bioinformatics,2009,10(211):1-10 25. Rattray, Magnus,Liu, Xuejun,Sanguinetti, Guido,Milo, Marta,Lawrence, Neil D.,Propagating uncertainty in microarray data analysis,Briefings in Bioinformatics,2006,7(1):37-47