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基本信息 The basic information
姓名: 杜磊

学院: 自动化学院


学历: 博士研究生毕业

学位: 工学博士


职称: 副教授

职务:


学科: 控制科学与工程-模式识别与智能系统,计算机科学与技术-计算机软件与理论,计算机科学与技术-计算机应用技术


邮箱: dulei@nwpu.edu.cn




工作经历 Work Experience
2016.03 - 至今 西北工业大学自动化学院 助理教授、长聘副教授
2020.07 - 至今 西北工业大学自动化学院博士生导师
2018.07 - 至今 西北工业大学自动化学院硕士生导师
2013.08 - 2016.01 美国印第安纳大学医学院 博士后



教育经历 Education Experience
2003.09 - 2007.07 西北工业大学自动化学院
2007.09 - 2013.06 西安交通大学电信学院



团队信息 Team Information
信息融合技术教育部重点实验室成员;自动化学院郭雷、韩军伟教授团队成员;与宾夕法尼亚大学医学院和印第安纳大学医学院保持长期的密切合作。
在读硕士：
2019级：刘方(发表TMI、Bioinformatics、MICCAI各一篇)、张金（发表Medical Image Analysis、BIBM各一篇）、王惠爱（投稿MICCAI一篇）
2020级：赵颖、余城林（投稿MICCAI一篇）、崔鼎男



招生信息 Admission Information
每年招收：博士研究生：2-3名 硕士研究生：2-3名
欢迎自动化、计算机科学与技术、生物医学工程、数学及电子信息学科的同学，尤其是对机器学习、数据挖掘、优化算法、生物信息学、医学图像分析等研究方向感兴趣的优秀同学保送/报考。课题组提供宽松的科研环境，并常年与国外合作，提供国内外交流机会。欢迎来信咨询：dulei@nwpu.edu.cn（请附上成绩单）。





社会兼职 Social Appointments
BIBM 2018Session Chair
BI 2017、 BI 2019Special Session Chair
BIBM 2019、BIBM 2020、BIBM 2021PC Member
中国计算机学会生物信息学专委委员
中国自动化学会智能健康与生物信息专委委员
中国自动化学会人工智能科普工作委员会委员
CCF会员、CAA会员、ISCB会员、MICCAI会员、IEEE会员
领域顶级期刊及会议如TMI、Medical Image Analysis、Bioinformatics、Briefings in Bioinformatics、Human Brain Mapping、Brain Imaging Behavior、TCBB、JBHI、IPMI、MICCAI、BIBM等审稿人。



科学研究 Scientific Research
研究方向为数据挖掘与机器学习、影像遗传学(Imaging Genetics)或影像基因组学(Imaging Genomics）、生物信息学、医学图像处理、精准医学、数据流挖掘等。主持国家自然科学基金面上项目（60万）、青年项目等10项科研项目。
科研项目（主持NSFC面上项目、青年项目各一项，省部级项目5项）：

[1] 2020-2023国家自然科学基金面上项目 基于多任务多视角的影像遗传学分析方法研究（No.**，主持）
[2] 2020-2021 陕西省自然科学基础研究计划面上项目 面向影像基因组学的多视角学习方法（No.2020JM-142，主持）
[3]2020-2021计算神经科学与类脑智能教育部重点实验室开放课题 多模态、纵向脑影像基因组学计算方法研究 （主持）
[4] 2017-2019国家自然科学基金青年项目 影像遗传学中海量数据挖掘算法研究及其在老年痴呆症中的应用（No.**，主持）
[5] 2017-2018陕西省自然科学基础研究计划青年项目 超高维影像基因组学分析方法研究（No.2017JQ6001，主持）
[6]2年度留学回国人员科技活动项目择优资助 脑影像-全基因组关联分析中的大数据挖掘方法研究(No.**，主持)
[7] 2017-2018中国博士后科学基金面上项目 影像基因组学中的高效多视角学习方法研究（No.2017M613202，主持）
[8] 2017-2018 陕西省博士后科研资助项目 影像基因组学中的优化算法研究(No. 2017BSHEDZZ81，主持)
[9] 2018-2019 中央高校基本科研业务费（主持）

[10] 2019-2023国家自然科学重点项目 基于脑成像的视听深度神经网络构建与应用（No.**，291万，参与）
[11] 2020-2024国家自然科学重点项目 时空多尺度动态脑功能网络的深度神经网络分析方法及应用（No. **，300万，参与）



学术成果 Academic Achievements

以第一作者发表学术论文30余篇(* 通讯作者； # 共同第一作者； 指导的硕士)，部分论文的代码可在https://github.com/dulei323下载。
2021:

[1]Lei Du*, Jin Zhang,Fang Liu, Huiai Wang, Lei Guo, Junwei Han. Identifying Associations among Genomic, Proteomic and Imaging Biomarkers via Adaptive Sparse Multi-view Canonical Correlation Analysis.Medical Image Analysis(IF = 11.14,Topjournal). accepted, Feb. 2021.
[2]Lei Du*, Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Junwei Han, Andrew J. Saykin,Lei Guo, Li Shen*. Multi-Task Sparse Canonical Correlation Analysis with Application to Multi-Modal Brain Imaging Genetics.IEEE/ACM Transactions on Computational Biology and Bioinformatics(IF = 2.896, CCF B类).vol. 18, no. 1, pp. 227-239, 2021, doi: 10.1109/TCBB.2019.**.
2020:

[1]Lei Du*,Fang Liu,Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Junwei Han, Andrew J. Saykin,Li Shen*. Associating Multi-modal Brain Imaging Phenotypes and Genetic Risk Factors via A Dirty Multi-task Learning Method.IEEE Transactions on Medical Imaging (IF = 7.82,Topjournal). vol. 39, no. 11, pp. 3416-3428, Nov. 2020, doi: 10.1109/TMI.2020.**.
[2]Lei Du*, Fang Liu,Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Junwei Han, Lei Guo,Andrew J. Saykin, Li Shen*. Identifying diagnosis-specific genotype-phenotype associations via joint multi-task sparse canonical correlation analysis and classification.Bioinformatics(IF = 5.61,Topjournal). [ISMB 2020 Issue, 19.4%acceptance rate], July 1, 2020.DOI: 10.1093/bioinformatics/btaa434.
[3]Lei Du*, Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Junwei Han, Andrew J. Saykin,Lei Guo,Li Shen*. Detecting genetic associations with brain imaging phenotypes in Alzheimer's disease via a novel structured SCCA approach. Medical Image Analysis(IF = 11.14,Topjournal). Volume 61, 101656, 2020.
[4]Lei Du*,Jin Zhang,Fang Liu,Minjianan Zhang,Huiai Wang,Lei Guo, Junwei Han. Mining High-order Multimodal Brain Image Associations via Sparse Tensor Canonical Correlation Analysis.IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Seoul, South Korea, December 16-19, 2020.(CCF B类)
[5]Tuo Zhang, Zhibin He, Xi Jiang, Lei Guo, Xiaoping Hu, Tianming Liu,Lei Du*. Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-Task Regression. The 23^rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)（医学图像领域顶级会议）, Lima, Peru, October 4-8, 2020. [15% early acceptance rate]
2019:
[1]Lei Du*, Kefei Liu, Lei Zhu, Xiaohui Yao, Shannon L. Risacher, Lei Guo, Andrew J. Saykin, Li Shen*. Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort.Bioinformatics (IF = 5.41, Top journal). [ISMB/ECCB 2019 Issue, 18.9%acceptance rate]. July, 2019.DOI:10.1093/bioinformatics/btz320.
[2]Lei Du*, Fang Liu, Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Junwei Han, Lei Guo, Andrew J. Saykin, Li Shen.A dirty multi-task learning method for multi-modal brain imaging genetics. The 22^nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)（医学图像领域顶级会议）, Shenzhen, China, October 13-17, 2019. [31% acceptance rate]
[3]Lei Du*, Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Lei Guo, Andrew J. Saykin, Li Shen. Diagnosis Status Guided Brain Imaging Genetics via Integrated Regression and Sparse Canonical Correlation Analysis.IEEE International Symposium on Biomedical Imaging (ISBI). Venice, Italy, April 8-11, 2019.
2018:
[1]Lei Du*, Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Junwei Han, Lei Guo, Andrew J. Saykin, Li Shen. Fast multi-task SCCA learning with feature selection for multi-modal brain imaging genetics.IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Madrid, Spain, December 3-6, 2018.(CCF B类，Best Paper Award)
[2]Lei Du, Kefei Liu, Tuo Zhang, Xiaohui Yao, Jingwen Yan, Shannon L. Risacher, Junwei Han, Lei Guo, Andrew J. Saykin, Li Shen*. A Novel SCCA Approach via Truncated L1-norm and Truncated Group Lasso for Brain Imaging Genetics.Bioinformatics: 2018,34(2), pp. 278-285. (IF = 7.307, Top journal)
2017:
[1] Lei Du*,Kefei Liu, Xiaohui Yao, JingwenYan, Shannon L. Risacher, Junwei Han, Lei Guo, Andrew J. Saykin, Li Shen*.Pattern Discovery in Brain Imaging Genetics via SCCA Modeling with a GenericNon-convex Penalty. Scientific Reports: 2017.10, accepted. (IF = 4.2589)
[2] Lei Du*, Tuo Zhang, Kefei Liu,Jingwen Yan, Xiaohui Yao, Shannon L. Risacher, Andrew J. Saykin, Junwei Han,Lei Guo, Li Shen*. Identifying Associations Between Brain Imaging Phenotypes andGenetic Factors via A Novel Structured SCCA Approach. The 25^th BiennialInternational Conference on Information Processing in Medical Imaging (IPMI),Boone, USA, June 24-30, 2017 (医学图像领域顶级会议)
[3] Yuming Huang, Lei Du*, Kefei Liu, Xiaohui Yao,Shannon L. Risacher, Lei Guo, Andrew J. Saykin, Li Shen. A fast SCCA algorithmfor big data analysis in brain imaging genetics. MICCAI Workshop onImaging Genetics (MICGen), 9 pages, September 10, 2017. (Corresponding author)
[4] Xiao Li, Tuo Zhang, QinglinDong, Shu Zhang, Xintao Hu, Lei Du,Lei Guo, Tianming Liu. Predicting cortical 3-hinge locations via structuralconnective features. IEEE International Symposium on Biomedical Imaging (ISBI). 2017: 533-537
2016:
[1] Lei Du, Heng Huang, Jingwen Yan,Sungeun Kim, Shannon L. Risacher, Mark Inlow, Jason H. Moore, Andrew J. Saykin,Li Shen*. Structured Sparse Canonical Correlation Analysis for Brain ImagingGenetics: An Improved GraphNet Method. Bioinformatics: 2016, 32(10), pp.1544-1551 (IF = 7.307, Top journal)
[2] Lei Du, Heng Huang, Jingwen Yan,Sungeun Kim, Shannon L. Risacher, Mark Inlow, Jason H. Moore, Andrew J. Saykin,Li Shen*. Structured sparse CCA for brain imaging genetics via graph OSCAR. BMCSystems Biology: 2016,10(3) ,335-345 (IF = 2.303)
[3] Lei Du, Tuo Zhang, Kefei Liu,Xiaohui Yao, Jingwen Yan, Shannon L. Risacher, Lei Guo, Andrew J. Saykin, LiShen^*. Sparse Canonical Correlation Analysis via truncated -norm withapplication to brain imaging genetics. IEEE International Conference onBioinformatics and Biomedicine (BIBM), Shenzhen, China, Dec. 14-18, 2016, pp:707-711
[4] Xiao Li^#, Lei Du^#, Tuo Zhang^#, Xintao Hu, Xi Jiang, Lei Guo, TianmingLiu*. Species Preserved and Exclusive Structural Connections Revealed by SparseCCA. The19^th International Conference Medical Image Computing andComputer-Assisted Intervention (MICCAI), Athens, Greece, October 17-21, 2016,Proceedings, Part I: 2016, pp: 123-131 (^#equal contribution, 医学图像领域顶级会议)
[5] JingwenYan,LeiDu,S. L.Risacher,LiShen, A. J.Saykin. Identification of diagnosisrelated imaging genomics associations through outcome-guided sparse CCA: AnAlzheimer’s disease study. The 12^th International Conference Imaging GeneticsConference (IIGC), Irvine, CA, Jan 18-19, 2016.
2015:
[1] Lei Du*, Qinbao Song, Lei Zhu,Xiaoyan Zhu. A Selective Detector Ensemble for Concept Drift Detection. TheComputer Journal: 2015 ,58(3) ,457—471 (IF = 1.00)
[2] Lei Du, Jingwen Yan, Sungeun Kim,Shannon L. Risacher, Heng Huang, Mark Inlow, Jason H. Moore, Andrew J. Saykin,Li Shen. GN-SCCA: GraphNet Based Sparse Canonical Correlation Analysis forBrain Imaging Genetics. The 8^thInternational Conference onBrain Informatics and Health (BIH): 2015,pp: 275-284
[3] Du, Lei^*and Chakraborty, A*and Chiang CW and Cheng, L and Quinney SK and Wu HY and Zhang P and Li Lang,and Shen Li. Graphic mining of high-order drug interactions and theirdirectional effects on myopathy using electronic medical records. CPT:Pharmacometrics & Systems Pharmacology, 2015, 4(8):481-488. DOI:10.1002/psp4.59. (* equal contribution)
[4] Zhang P* and Du, Lei* and Wang L and Liu M and Cheng L and ChiangCW and Wu HY and Quinney SK and Shen, Li and Li Lang. A mixture dose-responsemodel for identifying high-dimensional drug interaction effects on myopathyusing electronic medical record databases. CPT: Pharmacometrics & SystemsPharmacology, 2015, 4(8):474-480. DOI: 10.1002/psp4.53. (* equalcontribution)
[5] Yan Jingwen and Du, Lei and Kim, S and Risacher,SL and Huang, H and Inlow, M and Moore, JH and Saykin, AJ and Shen, Li, for theADNI. (2015) BoSCCA: Mining stable imaging and genetic associations withimplicit structure learning. MICCAI Workshop on ImagingGenetics (MICGen), October 9, 2015.
2014:
[1] Lei Du*, Qinbao Song, Xiaolin Jia.Detecting concept drift: An information entropy based method using an adaptivesliding window. Intelligent data analysis: 2014 ,18(3) ,337--364 (IF = 0.631)
[2] Lei Du, Jingwen Yan, Sungeun Kim,Shannon L. Risacher, Heng Huang, Mark Inlow, Jason H. Moore, Andrew J. Saykin,Li Shen*. A Novel Structure-aware Sparse Learning Algorithm for Brain ImagingGenetics. The 17^th International Conference on Medical Image Computing andComputer-Assisted Intervention (MICCAI), Boston, USA, Sep. 14-18, 2014, pp:329-336 (Co-first author, 医学图像领域顶级会议)
[3] Jingwen Yan, Lei Du, Sungeun Kim, Shannon L.Risacher, Heng Huang, Jason H. Moore, Andrew J. Saykin, Li Shen.Transcriptome-guided amyloid imaging genetic analysis via a novel structuredsparse learning algorithm. Bioinformatics: 2014 ,30(17),i564--i571
[4] Yan, Jingwen and Zhang, Hui andDu, Lei and Wernert, E andSaykin, AJ and Shen, Li (2014) Accelerating sparse canonical correlationanalysis for large brain imaging genetics data. The Annual ExtremeScience and Engineering Discovery Environment Conference (XSEDE). Atlanta, GA, July 13-18, 2014. doi 10.1145/**.**.
[5] Yao, Xiaohui and Chen, Rui andKim, S and Yan J and Du, Leiand Nho, K and Foroud, TM and Moore, JH and Weiner, MW and Saykin, AJ and Shen,Li. Genetic Findings using ADNI Multimodal Quantitative Phenotypes: A Review ofPapers Published in 2013. Alzheimer's Association International Conference on Alzheimer's Disease (AAIC), Copenhagen, Denmark, July 12-17, 2014.
[6] Zhu, Lei and Song, Qinbao andGuo, Yuchen and Du, Lei andZhu, Xiaoyan and Wang, Guangtao. A Coding Method for Efficient Subgraph Queryingon Vertex- and Edge-Labeled Graphs. PLOS ONE: 2014 ,9(5)
2013:
[1] 杜磊, 杜星, 宋擒豹. 一种k-NN分类器k值自动选取方法. 控制与决策: 2013 ,28(7) ,1073. (中国科技期刊卓越行动计划梯队期刊)
[2] Lei Du, Qinbao Song. A SimpleClassifier Based on a Single Attribute. The 14^th IEEE InternationalConferences on High Performance Computing and Communications (HPCC).Liverpool, UK, Jun. 24-28, 2012, pp: 660-665
Book Chapter:
[1] Yan, Jingwen^# and Du, Lei^# and Yao, Xiaohui^#andShen, Li. Book Chapter in Machine Learning and Medical Imaging:Machine learning in brain imaging genomics. Elsevier: 2016. (^#equalcontribution)



English Version