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李小伟


【来源:ued西甲赫塔菲官网 | 发布日期:2019-03-04 】     【选择字号:

   名:李小伟

政治面貌:中共党员

    称:教授、博士生导师

    务:数据科学研究中心主任

所在系所:计算机应用技术研究所

    lixwei@lzu.edu.cn

办公地址:飞云楼513

 

学习经历

1998.09-2002.07,兰州大学,计算机科学系, 工学学士

2002.09-2005.07,兰州大学, 计算机科学与技术,工学硕士

2009.09-2015.06, 兰州大学, 计算机科学与技术,工学博士

 

工作经历

2007.04-2013.04,uedbet,讲师

2013.05-2018.04,uedbet,副教授

2018.05-至今,uedbet,教授

 

教学情况

主讲本科生课程:

Web数据库技术》, C语言程序设计》, 《汇编语言》等

 

指导研究生情况

2014年以来指导硕士研究生15.

 

研究方向

研究领域为生物医学数据处理、普适情感计算、机器学习等。当前研究主要为抑郁症患者脑电信号、眼动信号分析处理。

 

招生专业

计算机科学与技术,计算机应用技术等相关专业.

 

项目成果

近五年主持或参加科研项目(课题)及人才计划项目情况:

1.自然科学基金重点项目, 61632014.

2.国家“973”计划, 2014CB744600.

3.国家自然科学基金重大项目, 61210010.

4.科技部国际(地区)合作交流项目, 2013DFA11140.

 

发表论文及专著

5年主要的SCI/EI论文如下:

[1]Li X, Hu B, Xu T, et al. A study on EEG-based brain electrical source of mild depressed subjects[J]. Computer methods and programs in biomedicine, 1 2 0 ( 2 0 1 5 ) 135–141.

[2]Li X, Hu B, Shen J, et al. Mild Depression Detection of College Students: an EEG-Based Solution with Free Viewing Tasks[J]. Journal of medical systems, 2015, 39(12): 187.

[3]Li X, Hu B, Sun S, et al. EEG-based mild depressive detection using feature selection methods and classifiers[J]. Computer Methods and Programs in Biomedicine, 2016, 136: 151-161.

[4]Li X, Cao T, Sun S, et al. Classification study on eye movement data: Towards a new approach in depression detection[C]//Evolutionary Computation (CEC), 2016 IEEE Congress on. IEEE, 2016: 1227-1232.

[5]Li X, Cao T, Hu B, et al. EEG Topography and Tomography (sLORETA) in Analysis of Abnormal Brain Region for Mild Depression[C]//International Conference on Brain and Health Informatics. Springer International Publishing, 2016: 304-311.

[6]Hu B, Li X, Sun S, et al. Attention Recognition in EEG-Based Affective Learning Research Using CFS+ KNN Algorithm [J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, PP(99):38-45, 2018. Volume 15 Issue 1

[7]Li X, Jing Z, Hu B, et al. An EEG-based study on coherence and brain networks in mild depression cognitive process[C]//Bioinformatics and Biomedicine (BIBM), 2016 IEEE International Conference on. IEEE, 2016: 1275-1282.

[8]Li X, Jing Z, Hu B, et al. A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering[J]. Complexity, 2017, 2017.

[9]Hu B, Rao J, Li X, et al. Emotion Regulating Attentional Control Abnormalities In Major Depressive Disorder: An Event-Related Potential Study[J]. Scientific reports, 2017, 7(1): 13530.

[10]Zhu J, Li J, Li X, et al. Neural basis of the emotional conflict processing in major depression: ERPs and source localization analysis on the N450 and P300 components[J]. Frontiers in Human Neuroscience, 2018, 12: 214.

[11]Xiaowei Li, Jianxiu Li, Bin Hu, et al. Attentional bias in MDD: ERP components analysis and classification using a dot-probe task. Computer Methods and Programs in Biomedicine. Volume 164, October 2018, Pages 169-179.

[12]Mao W, Zhu J, Li X, et al. Resting State EEG Based Depression Recognition Research Using Deep Learning Method[C]//International Conference on Brain Informatics. Springer, Cham, 2018: 329-338.

[13]Sun S, Li X, Zhu J, et al. Graph Theory Analysis of Functional Connectivity in Major Depression Disorder with High-Density Resting State EEG data[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019.

[14]Li X, La R, Wang Y, et al. EEG-based mild depression recognition using convolutional neural network[J]. Medical & biological engineering & computing, 2019: 1-12.

[15]Li X, Zhang X, Zhu J, et al. Depression recognition using machine learning methods with different feature generation strategies[J]. Artificial Intelligence in Medicine, 2019, 99: 101696.

 

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