Han Zhang
Associate Professor
Research Area: Intelligent Medicine
Office: Room 404, BME Building
Brief CV
Research Interests
Services to External Academic Communities
Lab Introduction


Han Zhang is an Associate Professor and Director of the Disease and Intelligent Analytics Lab (DIAL) in the School of Biomedical Engineering, ShanghaiTech University. His research focuses on brain network modeling and brain functional imaging studies on early development, aging, and associated diseases such as Alzheimer’s disease and related dementias. He has published over 110 papers in top-tier journals and conferences such as PNAS, Brain, Human Brain Mapping, NeuroImage, IEEE TMI/Trans Cybernetics, AAAI, and MICCAI. As PI or co-PI, he has received sponsorship of multiple NIH (National Institute of Health, USA) grants. He was also the core member and co-PI of the NIH Baby Connectome Project (BCP).

Dr. Zhang got his B.S. degree in E.E. from Zhejiang University and his Ph.D. in Cognitive Neuroscience from Beijing Normal University. He was a Faculty Investigator at Hangzhou Normal University before he worked at the University of North Carolina at Chapel Hill as a postdoctoral fellow, lecturer, and Assistant Professor. Before joining in ShanghaiTech, he was an Investigator and PI at the Institute of Brain-Intelligence Technology, Zhangjiang Lab, Shanghai.

  • Brain Network Modeling

  • Infant Brain Development

  • Brain Aging

  • Alzheimer's Disease Related Dementias

  • Brain tumor

  • Computer-Aided Diagnosis

  • Deep Learning on Medical Images


Scientific writing

Scientific presentation

Brain sciences and brain diseases


Senior Member, Institute of Electrical and Electronics Engineers (IEEE)

Associate Editor, Brain Informatics

Associate Editor, Frontiers in Oncology

Guest Associate Editor, Frontiers in Oncology

Online Committee member, Medical Image Computing Seminar

Associated Editor, PLOS ONE


Brain network modeling

  • Ghanbari, M., Zhou, Z., Hsu, L.-M., Han, Y., Sun, Y., Yap, P.-T., Zhang, H.*, Shen, D.*, Altered Connectedness of the Brain Chronnectome During the Progression to Alzheimer’s Disease, Neuroinformatics, 2021, Accepted

  • Li, G., Liu, Y., Zheng, Y., Wu, Y., Li, D., Liang, X., Chen, Y., Cui, Y., Yap, P.-T., Qiu, S.*, Zhang, H.*, Shen, D.*, 2021, Multiscale Neural Modeling of Resting-state fMRI Reveals Executive-Limbic Malfunction as a Core Mechanism in Major Depressive Disorder, NeuroImage: Clinical, 2021, 31: 102758.

  • Zhou, Z., Chen, X., Zhang, Y., Hu, D., Qiao, L., Yu, R., Yap, P.-T., Pan, G.*, Zhang, H.*, Shen, D.*, A Toolbox for Brain Network Construction and Classification (BrainNetClass). Human Brain Mapping, 2020, 41(10): 2808-2826.

  • Li, G., Liu, Y., Zheng, Y., Li, D., Liang, X., Chen, Y., Cui, Y., Yap, P.-T., Qiu, S.*, Zhang, H.*, Shen, D.*, Large-scale Dynamic Causal Modeling of Major Depressive Disorder based on Resting-state fMRI. Human Brain Mapping, 2019.

  • Yu, R.#Zhang, H.#, An, L., Chen, X., Wei, Z., Shen, D., 2017. Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification. Human Brain Mapping, 38(5): 2370-2383.


Computer-aided diagnosis

  • Chen, Y.#, Zhou, Z.#, Liang, Y., Tan X., Li, Y., Qin, C., Feng, Y., Ma, X., Mo, Z., Xia, J., Zhang, H.*, Qiu, S.*, Shen, D.*, Classification of Type 2 Diabetes Mellitus with or without Cognitive Impairment from Healthy Controls Using High-order Functional Connectivity. Human Brain Mapping, 2021, 42(14): 4671-4684.

  • Jiang, W., Zhang, H.*, Zeng, L.-L., Shen, H., Jian, Q., Thung, K.-H., Yap, P.-T., Liu, H., Hu, D., Wang, W.*, Shen, D.*, Dynamic neural circuit disruptions associated with antisocial behaviors. Human Brain Mapping, 2021, 42 (2): 329-344

  • Ding, Z.#, Zhang, H.#, Lv, X.#, Xie, F., Liu L., Li, L., Shen, D., 2018. Radiation-induced Brain Structural and Functional Abnormalities in Pre-symptomatic Phase and Outcome Prediction. Human Brain Mapping, 39(1):407-427.

  • Chen, X.#Zhang, H.#, Zhang, L., Shen, C., Lee, S.-W., Shen, D., 2017. Extraction of Dynamic Functional Connectivity from Brain Grey Matter and White Matter for MCI Classification, Human Brain Mapping, 38(10):5019-5034.


Alzheimer’s disease and related dementias

  • Yue, L.#, Hu, D.#, Zhang, H.#, Wen, J., Wu, Y., Li, W., Sun, L., Li, X., Wang, J., Li, G., Wang, T., Shen, D., Xiao, 2021, Prediction of 7-year’s Conversion from Subjective Cognitive Decline to Mild Cognitive Impairment. Human Brain Mapping. DOI: 10.1002/hbm.25216.

  • Kam, T.-E., Zhang, H.*, Jiao, Z., Shen, D., Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection, IEEE Transactions on Medical Imaging, 2020, 39(2): 478–487.

  • Ghanbari, M., Hsu, L.-M., Zhou, Z., Ghanbari, A., Mo, Z., Yap, P.-T., Zhang, H.*, Shen, D.*, A New Metric for Characterizing Dynamic Redundancy of Dense Brain Chronnectome and Its Application to Early Detection of Alzheimer's Disease, MICCAI 2020, Lima, Peru, Oct 4-8, 2020 (MICCAI 2020 NIH Award).

  • Jiao, Z., Huang, P., Kam, T.-E., Hsu, L.-M., Wu, Y., Zhang, H.*, Shen, D.*, Dynamic Routing Capsule Networks for Mild Cognitive Impairment Diagnosis, MICCAI 2019, Shenzhen, China, Oct 13-17, 2019.


Infant brain development

  • Wen, X., Wang, R., Yin, W., Lin, W., Zhang, H.*, Shen, D.*, Development of Dynamic Functional Architecture during Early Infancy. Cerebral Cortex2020, 30(11): 5626-5638.

  • Wen, X., Zhang, H.*, Li, G., Liu, M., Yin, W., Lin, W., Zhang, J., Shen, D.*, First-Year Development of Modules and Hubs in Infant Brain Functional Networks. NeuroImage, 2019, 185:222-235.

  • Zhang, H., Shen, D., Lin, W., 2019. Resting-state Functional MRI Studies on Infant Brains: a Decade of Gap-Filling Efforts, NeuroImage, 185:664-684.

  • Sousia, M., Wen X., Zhou, Z., Jin, B., Kam, T.-E., Hsu, L.-M., Wu, Z., Li, G., Wang, L., Rekik, I., Lin, W., Shen, D., Zhang, H.*, and for UNC/UMN Baby Connectome Project Consortium, A computational framework for dissociating development-related from individually variable flexibility in regional modularity assignment in early infancy, MICCAI 2020, Lima, Peru, Oct 4-8, 2020.

  • Jiang, W., Zhang, H.*, Wu, Y., Hsu, L.-M., Hu, D., Shen, D.*, Early Development of Infant Brain Complex Network, MICCAI 2019, Shenzhen, China, Oct 13-17, 2019.

  • Zhou, Z., Zhang, H.*, Hsu, L.-M., Lin, W., Pan, G.*, Shen, D.*, and for the UNC/UMN Baby Connectome Project Consortium, Multi-layer temporal network analysis reveals increasing temporal reachability and spreadability in the first two years of life, MICCAI 2019, Shenzhen, China, Oct 13-17, 2019.

  • Kam, T.-E., Wen, X., Jin, B., Jiao, Z., Hsu, L.-M., Zhou, Z., Liu, Y., Yamashita, K., Hung, S.-C., Lin, W., Zhang, H.*, Shen, D.*, and for UNC/UMN Baby Connectome Project Consortium, A Deep Learning Framework for Noise Component Detection from Resting-state Functional MRI, MICCAI 2019, Shenzhen, China, Oct 13-17, 2019.

  • Zhang, H., Stanley, N., Mucha, P.J., Yin, W., Lin, W., Shen, D., Multi-layer large-scale functional connectome reveals infant brain developmental patterns. MICCAI 2018, Granada, Spain, Sep. 16-20, 2018.


Brain tumors

  • Tang, Z., Xu, Y., Jin, L., Aibaidula, A., Lu, J., Jiao, Z., Wu, J.*, Zhang, H.*, Shen, D.*, Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients. IEEE Transactions on Medical Imaging, 2020, 39(6): 2100-2109.

  • Chen, L.#, Zhang, H.#,Lu, J.#, Thung, K., Aibaidula, A., Liu, L., Chen, S., Jin, L., Wu, J., Wang, Q., Zhou, L., Shen, D., 2018. Multi-label Nonlinear Matrix Completion with Transductive Multi-task Feature Selection for Joint MGMT and IDH1 Status Prediction of Patient with High-Grade Gliomas. IEEE Transactions on Medical Imaging, 37(8):1775-1787.

  • Cao, B., Zhang, H.*, Wang, N.*, Gao, X., Shen, D.*, Auto-GAN: Self-Supervised Collaborative Learning for Medical Image Synthesis, AAAI 2020, New York, USA, Feb 7-12, 2020 (Travel award).

  • Huang, P., Li, D., Jiao, Z., Wei, D., Li, G., Zhang, H.*, Shen, D.*, CoCa-GAN: Common-feature-learning-based Context-aware Generative Adversarial Network for Glioma Grading, MICCAI 2019, Shenzhen, China, Oct 13-17, 2019.


  • Brain network modeling for disease studies: including novel algorithm development, such as high-order network, dynamic network and directed network, for more accurate individualized disease diagnosis.

  • Machine learning on brain functional images: developing novel machine learning techniques learning spatiotemporal characteristics from the brain networks constructed based on structural and functional imaging.

  • Brain tumor studies: multimodality imaging-based brain tumor segmentation, grading, functional localization, outcome prediction, image synthesis, imaging genomics, and surgical planning.

  • Infant brain development: large-scale infant brain imaging database construction, early brain functional, structural, connectomics development, construction of normative development trajectories, association study on gene-environment-imaging-behavior.

  • Brain aging and aging-related diseases: construction of brain aging database, aging-related brain functional, structural, connectomics changes and their associations with cognitive functions and diseases.