Qing Li, Ph.D., Postdoc Fellow

E-mail: liqing#bnu.edu.cn

RESEARCH INTERESTS

  • Brain functional network analysis with deep models (e.g. neural architecture search)

  • Brain functional mechanism inspired algorithms

  • Multi-dimensional brain function development

EDUCATION

  • 2018.10-2020.10: Visiting Research Scholar in Computer Science, Department of Computer Science, the University of Georgia

  • 2015.09-2022.01: Ph.D. in Computer Application Technology, School of Artificial Intelligence (College of Information Science and Technology), Beijing Normal University

  • 2011.09-2015.06: B.Sc. major in Computer Science & Technology, minor in English Application, College of Information Engineering, Capital Normal University

FUNDING

  • Neural Architecture Search Algorithms for Cognitive Task-specific Brain Age Prediction, National Natural Science Foundation of China, No. 62206024, 01/2023-12/2025, Role on project: PI.

  • Brain State Decoding and Classification via Deep Learning Models, the Fundamental Research Funds of the Central Universities, No. 2017STU34, 01/2017-12/2017, Role on project: PI.

SELECTED PUBLICATIONS

  • Li Q, Xu X, Wu X*. Spatio-temporal Co-variant Hybrid Deep Learning Framework for Cognitive Performance Prediction (2022). Acta Automatica Sinica (自动化学报). 48(12): 2931−2940

  • Li Q, Zhang W, Zhao L, Wu X*, Liu T*. Evolutional Neural Architecture Search for Optimization of Spatiotemporal Brain Network Decomposition (2022). IEEE Transactions on Biomedical Engineering. 69(2): 624-634. [Featured Article]

  • Li Q, Wu X*, Liu T*. Differentiable Neural Architecture Search for Optimal Spatial/temporal Brain Function Network Decomposition (2021). Medical Image Analysis. 69: 101974.

  • Wu X*, Li Q, Xu L, Chen K, Yao L. Multi-feature Kernel Discriminant Dictionary Learning for Face Recognition (2017). Pattern Recognition. 66: 404-411.

  • Li Q#, Dong Q#, Ge F, Qiang N, Wu X*, Liu T*. Simultaneous Spatial-temporal Decomposition for Connectome-scale Brain Networks by Deep Sparse Recurrent Auto-encoder (2021). Brain Imaging and Behavior. 15: 2646–2660.

  • Li Q, Wu X*, Xie F, Chen K, Yao L, Zhang J, Guo X, Li R, Alzheimer's Disease Neuroimaging Initiative. Aberrant Connectivity in Mild Cognitive Impairment and Alzheimer’s Disease Revealed by Multimodal Neuroimaging Data (2018). Neurodegenerative Diseases. 18(1):5-18.

  • Li Q, Wu X*, Xu L, Chen K, Yao L, Alzheimer's Disease Neuroimaging Initiative. Classification of Alzheimer’s Disease, Mild Cognitive Impairment, and Cognitively Unimpaired Individuals using Multi-feature Kernel Discriminant Dictionary Learning (2018). Frontiers in Computational Neuroscience. 11:117.

  • Li Q, Wu X*, Xu L, Chen K, Yao L, Li R. Multi-modal Discriminative Dictionary Learning for Alzheimer's Disease and Mild Cognitive Impairment (2017) Computer Methods and Programs in Biomedicine. 150: 1-8.

  • Wu X, Li Q, Yu X, Chen K, Fleisher A S, Guo X, Zhang J, Reiman E M, Yao L, Li R*. A Triple Network Connectivity Study of Large-Scale Brain Systems in Cognitively Normal APOE4 Carriers (2016). Frontiers in Aging Neuroscience. 8: 231.

  • Dai H#, Li Q#, Zhao L, Pan L, Shi C, Liu Z, Wu Z, Zhang L, Zhao S, Wu X, Liu T, Zhu D*. Graph Representation Neural Architecture Search for Optimal Spatial/Temporal Functional Brain Network Decomposition, MICCAI-MLMI 2022: 279-287.

  • Li Q, Zhang W, Lv J, Wu X*, Liu T. Neural Architecture Search for Optimization of Spatial-temporal Brain Network Decomposition, MICCAI 2020: 377-386.

  • Li Q#, Dong Q#, Ge F, Qiang N, Zhao Y, Wang H, Huang H, Wu X*, Liu T*. Simultaneous Spatial-temporal Decomposition of Connectome-scale Brain Networks by Deep Sparse Recurrent Auto-encoders, IPMI 2019: 579-591.

  • Li Q, Wu X*, Xu L, Yao L, Chen K. Multi-feature Kernel Discriminant Dictionary Learning for Classification in Alzheimer’s Disease, DICTA 2017 : 1-8.

  • Li Z, Zhu Z, Li Q, Wu X*. Jointly Fusing Multi-scale Spatial-logical Brain Networks: A Neural Decoding Method (2022). IEEE Journal of Biomedical and Health Informatics, Accepted.

  • Li X, Liu T, Li Y, Li Q, Wang X, Hu X, Guo L, Zhang T*, Liu T*. Marmoset Brain ISH Data Revealed Molecular Difference Between Cortical Folding Patterns (2021). Cerebral Cortex. 31(3): 1660-1674.

  • Xu X, Jia T, Li Q, Wei F, Ye L, Wu X*. EEG Feature Selection via Global Redundancy Minimization for Emotion Recognition (2021). IEEE Transactions on Affective Computing, Accepted.

  • Zhang H, Li R, Wen X, Li Q, Wu X*. Altered Time-Frequency Feature in Default Mode Network of Autism Based on Improved Hilbert-Huang Transform (2020). IEEE Journal of Biomedical and Health Informatics, 25(2): 485-492.

See Google Scholar for more information: https://scholar.google.com/citations?user=yNW6l7EAAAAJ&hl=zh-CN