1、个人基本信息：

姓  名：邝利丹

性  别：女

出生年月：1989.07

技术职称：副教授

毕业院校：大连理工大学

学历（学位）：博士

所在学科：信号与信息处理

研究方向：盲源分离、脑信号处理（fMRI、fNIRS）、计算机视觉

2、教育背景：

2008.09----2012.06

湘潭大学

学士

2012.09----2018.10

大连理工大学

博士（硕博连读）

3、 目前研究领域：

1） 基于盲源分离方法的多被试fMRI和fNIRS数据组分析；

2） 脑功能信号提取与改变分析；

3） 目标检测和跟踪。

4、已完成或已在承担的主要课题：

主持国家自然科学基金青年项目1项，湖南省自然科学基金面上项目1项、青年项目1项，湖南省教育厅优秀青年项目1项、一般项目1项；作为主要成员参与国家自然科学基金项目多项：

1） 基于耦合张量分解的高维多被试复数fMRI数据分析，国家自然科学基金青年项目，61901061，2020.1-2022.12，24.5万元，主持，已结题。

2） 基于张量分解的复值fMRI动态功能连接分析研究，湖南省自然科学基金面上项目，2025JJ50394，2025.1-2027.12，5万元，主持，在研。

3） 基于张量分解的复值静息态fMRI功能连接分析，湖南省教育厅科学研究优秀青年项目，22B0341，2022.1-2024.6，6万元，主持，已结题。

4） 基于联合盲源分离的多被试复值fMRI 数据分析，湖南省自然科学基金青年项目，2020JJ5603，2020.1-2023.12，5万元，主持，已结题。

5） 基于张量分解的高维多被试复数fMRI数据组分析方法研究，19C0031，湖南省教育厅科学研究一般项目，2019-2021，1万元，主持。

6） 基于异构数据融合的网络异常检测方法研究，62272062，国家自然科学基金面上项目，2023.1-2026.12，主要参与，在研。

7） 视觉跟踪中目标深度表观模型的学习与更新方法研究，61972056，国家自然科学基金面上项目，2020.1-2023.12，主要参与，在研。

8） 空间源相位约束下完备复数fMRI数据的稀疏表示，61871067，国家自然科学基金面上项目，2019.1 - 2022.12，主要参与，在研。

9） 基于耦合张量分解的多数据集联合盲分离方法研究，61671106，国家自然科学基金面上项目，2017.1 - 2020.12，主要参与，在研。

10） 基于复值ICA和张量分解的完备fMRI数据分析方法研究，61379012，国家自然科学基金面上项目，2014.1 - 2017.12，主要参与，完成。

6、已发表的学术论文：

[1] Kuang LD (邝利丹), Zhang HP, Zhu H, He S, Li W, Gui Y, Zhang J, Zhang J. “Shift-invariant rank-(L, L, 1, 1) BTD with 3D spatial pooling and orthonormalization: Application to multi-subject fMRI data,” Biomedical Signal Processing and Control, vol. 92, article no. 106058, 2024. (SCI二区，IF: 4.9)

[2] Kuang LD (邝利丹), Li HQ, Zhang J, Gui Y, Zhang J. “Dynamic functional network connectivity analysis in schizophrenia based on a spatiotemporal CPD framework,” Journal of Neural Engineering, vol. 21, article no. 016032, 2024. (SCI三区，IF: 3.7)

[3] Kuang LD (邝利丹), Lin QH, Gong XF, Zhang J, Li W, Li F, Calhoun VD. “Constrained CPD of complex-valued multi-subject fMRI data via alternating rank-R and rank-1 least squares,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 2630-2640, 2022. (SCI二区，IF: 5.4 )

[4] Kuang LD (邝利丹), He ZM, Zhang J, Li F. “Coupled canonical polyadic decomposition of multi-group fMRI data with spatial reference and orthonormality constraints,” Biomedical Signal Processing and Control, vol. 80, no. 1, article no. 104232, 2023. (SCI二区，IF: 4.9)

[5] Kuang LD (邝利丹), Lin QH, Gong XF, Cong F, Wang YP, Calhoun VD. “Shift-invariant canonical polyadic decomposition of complex-valued multi-subject fMRI data with phase sparsity constraint,” IEEE Transactions on Medical Imaging, vol. 39, no. 4, pp. 844-853, 2020. (SCI一区，IF: 11.3)

[6] Kuang LD (邝利丹), Lin QH, Gong XF, Cong F, Sui J, Calhoun VD. “Model order effects on ICA of resting-state complex-valued fMRI data: Application to schizophrenia,” Journal of Neuroscience Methods, vol. 304, pp. 24−38, 2018. (SCI四区，IF: 2.7)

[7] Kuang LD (邝利丹), Lin QH, Gong XF, Cong F, Calhoun VD. “Adaptive independent vector analysis for multi-subject complex-valued fMRI data,” Journal of Neuroscience Methods, vol. 281, pp. 49−63, 2017. (SCI四区，IF: 2.7)

[8] Kuang LD (邝利丹), Lin QH, Gong XF, Cong F, Sui J, Calhoun VD. “Multi-subject fMRI analysis via combined independent component analysis and shift-invariant canonical polyadic decomposition,” Journal of Neuroscience Methods, vol. 256, pp. 127–140, 2015. (SCI四区，IF: 2.7)

[9] Han Y, Lin QH, Kuang LD (邝利丹), Gong XF, Cong F, Wang YP, Calhoun VD. “Low-rank Tucker-2 model for multi-subject fMRI data decomposition with spatial sparsity constraint,” IEEE Transactions on Medical Imaging, vol. 41, no. 3, pp. 667-679, 2022. (IF: 11.3)

[10] Li WX, Lin QH, Zhao BH, Kuang LD (邝利丹), Zhang CY, Han Y, Calhoun VD, “Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia,” Journal of Neuroscience Methods, vol. 403, article no. 110049, 2024. (SCI四区，IF: 2.7)

[11] Qiu Y, Lin QH, Kuang LD (邝利丹), Gong XF, Cong F, Wang YP, Calhoun VD. “Spatial source phase: A new feature for identifying spatial differences based on complex-valued resting-state fMRI data,” Human Brain Mapping, vol. 40, pp. 2662-2676, 2019. (SCI二区，IF: 4.7)

[12] Zhang CY, Lin QH, Kuang LD (邝利丹), Li, WX, Gong XF, Calhoun VD. “Sparse representation of complex-valued fMRI data based on spatiotemporal concatenation of real and imaginary parts, ” Journal of Neuroscience Methods, vol. 351, 109047, 2021. (SCI四区，IF: 2.7)

[13] Yu MC, Lin QH, Kuang LD (邝利丹), Gong XF, Cong F, Calhoun VD. “ICA of full complex-valued fMRI data using phase information of spatial maps,” Journal of Neuroscience Methods, vol. 249, pp. 75−91, 2015. (SCI四区，IF: 2.7)

[14] Cong F, Lin QH, Kuang LD (邝利丹), Gong XF, Astikanen P, Ristaniemi T. “Tensor decompoistion of EEG signals: A brief review, ” Journal of Neuroscience Methods, vol. 248, pp. 59–69, 2015. (SCI四区，IF: 2.7)

国际会议论文（全部EI检索）：

[15] Kuang LD (邝利丹), Wang B, Lin QH, Zhang HP, Zhang J, Li W, Li F, Calhoun VD. “An accelerated rank-(L,L,1,1) block term decomposition of multi-subject fMRI data under spatial orthonormality constraint,” in Proc. 47th IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP2022), Singapore, pp. 856–860, 2022. (信号处理领域顶级会议，CCF推荐会议B类，口头报告)

[16] Kuang LD (邝利丹), Hao-Peng Zhang, Jianming Zhang. “Weighted spatial pooling preprocessing for rank-( L,L,1,1) BTD with orthonormality: application to multi-subject fMRI data,” in IEEE International Joint Conference on Neural Networks (IJCNN 2023), Gold Coast, Australia, 2023. (CCF推荐会议C类，口头报告)

[17] Kuang LD (邝利丹), Gui Y, Li W. “Optimizing pcsCPD with alternating rank-R and rank-1 least squares: application to complex-valued multi-subject fMRI data,” in Proc 29th International Conference on Neural Information Processing (ICONIP 2022), New Delhi, India, 2022. (CCF推荐会议C类，口头报告)

[18] Kuang LD (邝利丹), He ZM. “Coupled shift-invariant tensorial spatial ICA applied to multi-group complex-valued task-related and resting-state fMRI data,” in International Conference on Image, Vision and Computing (ICIVC 2022), Xi’an, China, pp. 468-472, 2022. (EI会议，口头报告)

[19] Kuang LD (邝利丹), Tao JJ, Zhang J, Li F. “A novel multi-scale key-point detector using residual dense block and coordinate attention,” in International Conference on Neural Information Processing (ICONIP 2021), Bali, Indonesia, pp. 235-246, 2021. (CCF推荐会议C类，口头报告)

[20] Kuang LD (邝利丹), Lin QH, Gong XF, Cong F, Calhoun VD. “Post-ICA phase de-noising for resting-state complex-valued FMRI data,” in Proc. 42nd IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP2017), New Orleans, USA, pp. 856–860, 2017. (信号处理领域顶级会议，CCF推荐会议B类，口头报告)

[21] Kuang LD (邝利丹), Lin QH, Gong XF, Chen YG, Cong F, Calhoun VD. “Model order effects on independent vector analysis applied to complex-valued fMRI data,” in Proc. 14th IEEE Int. Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia, pp. 81–84, 2017. (医学图像处理领域顶级会议)

[22] Kuang LD (邝利丹), Lin QH, Gong XF, Cong F, Calhoun VD. “An adaptive fixed-point IVA algorithm applied to multi-subject complex-valued fMRI data,” in Proc. 41st IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP2016), Shanghai, China, pp. 714–718, 2016. (信号处理领域顶级会议，CCF推荐会议B类，口头报告)

[23] Kuang LD (邝利丹), Lin QH, Gong XF, J. Fan, Cong F, Calhoun VD. “Multi-subject fMRI data analysis: Shift-invariant tensor factorization vs. group independent component analysis,” in Proc. 1st IEEE China Summit and Int. Conf. Signal and Information Processing (ChinaSIP2013), Beijing, China, pp. 269–272, 2013. (IEEE会议，口头报告)

[24] Li WX, Zhang CY, Kuang LD (邝利丹), Han Y, Li HJ, Lin QH, Calhoun VD. “Marginal spectrum modulated hilbert-huang transform: application to time courses extracted by independent vector analysis of resting-state fMRI data,” in International Conference on Neural Information Processing (ICONIP 2021), Bali, Indonesia, pp. 235-246, 2021. (CCF推荐会议C类，口头报告)

[25] Niu YW, Lin QH, Qiu Y, Kuang LD (邝利丹), Calhoun VD. “Sample augmentation for classification of schizophrenia patients and healthy controls using ICA of fMRI data and convolutional neural networks,” In 2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP 2019), Marrakech, Morocco, pp. 297-302, 2021. (IEEE会议，口头报告)

[26] Qiu Y, Lin QH, Kuang LD (邝利丹), Zhao WD, Gong XF, Cong F, Calhoun VD. “Classification of schizophrenia patients and healthy controls using ICA of complex-valued fmri data and convolutional neural networks,” in Proc. 16th International Symposium on Neural Networks (ISNN 2019), Moscow, Russia, pp. 540-547, 2019. (IEEE会议，口头报告)

中国发明专利：

[27] 邝利丹, 林秋华, 龚晓峰, 丛丰裕, 一种适于多被试fMRI数据分析的快速移不变CPD方法, 专利号ZL201811510882.0, 2022.5.6, 已授权.

[28] 邝利丹, 林秋华, 张经宇, 龚晓峰, 丛丰裕. 多被试复数fMRI 数据移不变CPD分析方法，专利号ZL201910168387.4, 2022.7.19, 已授权.

[29] 邝利丹, 陶家俊, 张建明. 一种结合残差密集块与位置注意力的无锚框目标检测方法，专利号ZL20211073165.9, 2023.2.28, 已授权.

[30] 林秋华, 邝利丹, 龚晓峰, 丛丰裕, 一种用于多被试fMRI数据分析的分组张量方法,专利号ZL201410126455.8, 2017.1.18, 已授权.

[31] 林秋华, 邝利丹, 龚晓峰, 丛丰裕, 一种结合独立成分分析与移不变规范多元分解的多被试功能核磁共振成像数据分析方法, 专利号ZL201510510622.3, 2018.1.16, 已授权.

[32] 林秋华, 邝利丹, 龚晓峰, 丛丰裕, 一种适于多被试复值fMRI数据分析的自适应定点IVA算法, 专利号ZL201610165248.2, 2016.6.8, 已授权.

[33] 林秋华, 邝利丹, 龚晓峰, 丛丰裕, 对静息态复值fMRI数据进行ICA后处理消噪的相位精确范围检测方法, 专利号ZL201710116707.2, 2017.3.1, 已授权.

[34] 邝利丹, 龙磊, 一种空间压缩多被试fMRI的交替秩R和秩1移不变CPD算法, 专利号 ZL202211552769.5, 2022.12.6, 已公开.

科研奖励：

[35] 邝利丹, 林秋华, 龚晓峰, 丛丰裕, 2017年辽宁省优秀论文三等奖, 2017.09.19

[36] 邝利丹, 林秋华, 龚晓峰, 丛丰裕, 2016年辽宁省优秀论文三等奖, 2016.07.29

[37] 邝利丹, 林秋华, 龚晓峰, 丛丰裕, 2016年大连市优秀论文三等奖, 2016.07.26

7、 所获学术荣誉及学术影响：