温旭云，南京航空航天大学计算机科学与技术学院副教授，中山大学和美国北卡罗来纳大学教堂山分校计算机科学与技术专业联合培养博士。主要研究方向为人工智能与脑科学交叉领域，包括：脑启发的大模型机理解析、基于影像分析的脑发育智能模型构建等，已在相关领域发表学术论文40余篇，包括：IEEE TMI、IEEE TEC、IEEE TNSRE、NeuroImage、Cerebral Cortex、MICCIA、IEEE BIBM、ISBI等。担任IEEE TMI、IEEE TBME、IEEE TIM、IEEE TNNLS、Brain Research、Frontier in Neuroscience等国际期刊审稿人。主持和完成国家自然科学基金、科技创新2030“脑科学与类脑研究”婴幼儿重大项目子课题、和南京航空航天大学“人工智能”专项等；参与国家自然科学基金重点项目、以及基础加强计划技术领域基金重点项目等。获江苏省“双创博士”称号，现任中国图像图形学会脑图谱专业委员会委员、江苏省人工智能学会医学图像处理专委会委员等。担任SCI期刊Congenital Heart Disease青年编委。

欢迎对人工智能和脑科学交叉学科感兴趣的学生申报研究生！

部分期刊论文：

1. Wen, X., Chen, W. N., Lin, Y., Gu, T., Zhang, H., Li, Y., Zhang, J. (2017). A Maximal Clique Based Multiobjective Evolutionary Algorithm for Overlapping Community Detection. IEEE Transactions on Evolutionary Computation, 21(3), 363-377. (中科院一区TOP，被引用175次，影响因子16.497)

2. Wen, X., Zhang, H., Li, G., Liu, M., Yin, W., Lin, W., Shen, D. (2019). First-Year Development of Modules and Hubs in Infant Brain Functional Networks. Neuroimage, 185, 222-235. (中科院一区TOP，被引用79次，影响因子7.4)

3. Wen, X., Wang, R., Yin, W., Lin, W., Zhang, H., & Shen, D. (2020). Development of Dynamic Functional Architecture During Early Infancy. Cerebral cortex, 30(11), 5626-5638. (JCR Q1, 影响因子4.861)

4. Wen, X., Cao, Q., Zhang, D. (2023). Multi-Scale FC-based Multi-Order GCN: A Novel Model for Predicting Individual Behavior from fMRI. IEEE Transactions on Neural Systems and Rehabilitation Engineering. (JCR Q1, 影响因子4.528)

5. Wen, X., Wu, X., Liu, J., Li, K., & Yao, L. (2013). Abnormal Baseline Brain Activity in Non-Depressed Parkinson’s Disease and Depressed Parkinson’s Disease: A Resting-State Functional Magnetic Resonance Imaging Study. PloS one, 8(5), e63691. (JCR Q1, 被引用88次, 影响因子3.752)

6. Wen, X., Wu, X., Li, R., Fleisher, A. S., Reiman, E. M., Wen, X., Yao, L. (2013). Alzheimer's Disease-Related Changes in Regional Spontaneous Brain Activity Levels and Inter-Region Interactions in the Default Mode Network. Brain research, 1509, 58-65. (JCR Q1, 影响因子3.61)

7. Wen, X., Yang, M., Hsu, L., Zhang, D. (2022). Test-Retest Reliability of Modular-Relevant Analysis in Brain Functional Network. Frontiers in Neuroscience, 16, 1000863. (中科院二区TOP, 影响因子5.152)

8. Zhou, Y., Wang, P., Gong, P., Wei, F., Wen, X., Wu, X., Zhang, D. (2023). Cross-subject Cognitive Workload Recognition Based on EEG and Deep Domain Adaptation. IEEE Transactions on Instrumentation and Measurement, 72, 2518912. (通讯作者，JCR Q1，影响因子5.332)

9. Wen, X, Nie, Z., Cao, Q., Zhang, D. (2023) Community Detection in Complex Brain Networks: A Review. Journal of Frontiers of Computer Science and Technology.

10. Ma, K., Wen, X., Zhu, Q., Zhang, D. (2023). Ordinal Pattern Tree: A New Representation Method for Brain Network Analysis. IEEE Transactions on Medical Imaging. (中科院一区TOP，影响因子11.037)

11. Liang, C., Pearlson, G., Bustillo, J., Kochunov, P., Turner, J. A., Wen, X., Calhoun, V. D. (2023). Psychotic Symptom, Mood, And Cognition-Associated Multimodal MRI Reveal Shared Links To The Salience Network Within The Psychosis Spectrum Disorders. Schizophrenia Bulletin, 49(1), 172-184. (中科院一区TOP，影响因子9.213)

12. Hsu, L. M., Yang, J. T., Wen, X., Liang, X., Lin, L. C., Huang, Y. C., Tsai, Y. H. (2022). Human Thirst Behavior Requires Transformation of Sensory Inputs by Intrinsic Brain Networks. BMC biology, 20(1), 1-14. (中科院一区，影响因子7.364)

13. Wang, P., Gong, P., Zhou, Y., Wen, X., Zhang, D. (2022). Decoding the Continuous Motion Imagery Trajectories of Upper Limb Skeleton Points for EEG-Based Brain–Computer Interface. IEEE Transactions on Instrumentation and Measurement, 72, 1-12. (JCR Q1，影响因子5.332)

14. Zhou, Y., Xu, Z., Niu, Y., Wang, P., Wen, X., Wu, X., Zhang, D. (2022). Cross-Task Cognitive Workload Recognition based on EEG and Domain Adaptation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 50-60. (JCR Q1, 影响因子4.528)

15. Wu, X., Wen, X., Li, J., Yao, L. (2014). A New Dynamic Bayesian Network Approach for Determining Effective Connectivity From fMRI Data. Neural Computing and Applications, 24, 91-97. (JCR Q1, 影响因子5.102)

部分会议论文：

1. Wen, X., Cao, Q., Zhang, D. (2022). A Multi-Scale Multi-Hop Graph Convolution Network for Predicting Fluid Intelligence via Functional Connectivity. In 2022 IEEE International Conference on Bioinformatics and Biomedicine (生物医学影像顶会，CCF B类会议，接受率19.8%，Oral Presentation).

2. Wen, X., Zhang, D. (2021). A Multi-Layer Random Walk Method for Local Dynamic Community Detection in Brain Functional Network. IEEE International Conference on Bioinformatics and Biomedicine (生物医学影像顶会， CCF B类会议，接受率19.3%，Oral Presentation).

3. Lin, Y., Nie, D., Liu, Y, Yang, M., Zhang, D., Wen, X. (2023). Multi-Target Domain Adaptation with Prompt Learning for Medical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention (通讯作者，医学影像顶会，CCF B类会议).

4. Yang, M., Hsu, L. M., Qi, S., Zhang, D., Wen, X. (2023). A Multi-View Clustering-Based Method for Individual and Group Cortical Parcellations with Resting-State fMRI. IEEE 20th International Symposium on Biomedical Imaging (通讯作者，医学影像顶会).

5. Wen, X., Zhang. H., Wang. R., Lin, W., Shen, D. (2019). Increased Functional Connectivity Flexibility During Early Infancy, ISMRM. (Oral Presentation，会议前16%)

6. Wen, X., Zhang, H., Lin, W., Shen, D. (2018). Evolution of Brain Dynamics in the First 2 years of life, RSNA. (Oral Presentation， 会议前20%)

7. Wen, X., Zhang, H., Lin, W., Shen, D. (2018). Early Development of Modular Organization in Brain Functional Networks at Multiple Scales, ISMRM. (Oral Presentation，会议前15%).

8. Ma, K., Wen, X., Zhu, Q., Zhang, D. (2023). Positive Definite Wasserstein Graph Kernel for Brain Disease Diagnosis. International Conference on Medical Image Computing and Computer-Assisted Intervention (指导学生工作，医学影像顶会， CCF B类会议).

9. Ma, K., Wen, X., Zhu, Q., & Zhang, D. (2022). Optimal Transport Based Ordinal Pattern Tree Kernel for Brain Disease Diagnosis. International Conference on Medical Image Computing and Computer-Assisted Intervention (指导学生工作， 医学影像顶会，CCF B类会议).

10. Wang, X., Wen, X., Ma, K., Zhang, D. (2021). A Multilayer Maximum Spanning Tree Kernel For Brain Networks. IEEE 18th International Symposium on Biomedical Imaging (指导学生工作，医学影像顶会，Oral Presentation，会议前20%).

11. Soussia, M., Wen, X., Zhou, Z., Jin, B., Kam, T. E., Hsu, L. M., UNC/UMN Baby Connectome Project Consortium. (2020). A Computational Framework for Dissociating Development-Related from Individually Variable Flexibility in Regional Modularity Assignment in Early Infancy. International Conference on Medical Image Computing and Computer-Assisted Intervention (医学影像顶会，CCF B类会议).

12. Kam, T. E., Wen, X., Jin, B., Jiao, Z., Hsu, L. M., Zhou, Z., UNC/UMN Baby Connectome Project Consortium. (2019). A Deep Learning Framework for Noise Component Detection from Resting-State Functional MRI. International Conference on Medical Image Computing and Computer-Assisted Intervention (医学影像顶会，CCF B类会议).

13. Cao, Q., Wen, X. (2023) DouGNN: An End-to-End Deep Learning Framework for Predicting Individual Behaviors from fMRI Data. IEEE 2nd International Conference on Image Processing, Computer Vision and Machine Learning (ICICML).

14. Zhou, Y., Wang, P., Gong, P., Liu, Y., Wen, X., Wu, X., Zhang, D. (2022). Deep Domain Adaptation for EEG-Based Cross-Subject Cognitive Workload Recognition. In International Conference on Neural Information Processing.

15. Wen, X., Yao, L., Fan, T., Wu, X., Liu, J. (2012). The Spatial Pattern of Basal Ganglia Network: A Resting State fMRI Study. International Conference on Complex Medical Engineering. (Oral Presentation)