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Faculty
Aiying Zhang
Assistant Professor of Data Science
School of Data Science
Email: xsa5db@virginia.edu
Additional Contact Information
Website: aiying0512.github.io/
Office: 1919 Ivy Rd., Rm. 330
Aiying Zhang is an Assistant Professor of Data Science focused on the field of mental health. Prior to joining UVA, Aiying worked as a Postdoctoral Research Scientist at Columbia University and New York State Psychiatric Institute.
Her expertise lies in statistical modeling, multimodal neuroimaging, and genetics, with a focus on graphical models and network science. Dr. Zhang is interested in developing data-driven approaches leveraging multi-level information to understand typical and atypical brain development. She is working on various collaborative projects involving psychiatric disorders and neurological disorders.
Aiying received her B.S. degree in Statistics from University of Science and Technology and Ph.D. degree in Biomedical Engineering from Tulane University.
Education
- Ph.D., Biomedical Engineering, Tulane University
- B.S., Statistics, University of Science and Technology of China
Areas of Practice
Mental Health, Multimodal Brain Imaging, Genetics, Multi-omics, Lifespan Brain Development, Psychiatric/Neurological Disorders
Methodologies
Data Fusion & Integration, Causal Inference, Time Series Analysis, Graphical Models, Machine Learning/Deep Learning
Selected Publications
Zhang, G., Cai, B., Zhang, A., Tu, Z., Xiao, L., Stephen, J. M., Wilson, T. W., Calhoun, V. D. and Wang, Y. P. (2022). Detecting abnormal connectivity in schizophrenia via a joint directed acyclicgraph estimation model. NeuroImage, 260, 119451.
Cai, B., Zhang, G., Zhang, A., Xiao, L., Hu, W., Stephen, J. M., Wilson, T. W., Calhoun, V. D. andWang, Y.P. (2021) Functional connectome fingerprinting: identifying individuals and predictingcognitive functions via autoencoder. Human Brain Mapping 42.9, 2691-2705.
Hu, W., Meng, X., Bai, Y., Zhang, A., Cai, B., Stephen, J. M., Wilson, T. W., Calhoun, V. D. andWang, Y.P. (2021) Interpretable multimodal fusion networks reveal mechanisms of braincognition. IEEE Transactions on Medical Imaging, vol. 40, no. 5, pp. 1474-1483.
Xiao, L., Zhang, A., Cai, B., Stephen, J. M., Wilson, T. W., Calhoun, V. D. and Wang, Y.P. (2020)Correlation Guided Graph Learning to Estimate Functional Connectivity Networks from fMRIData. IEEE Transactions on Biomedical Engineering, vol. 68, no. 4, pp. 1154-1165.
Zhang, A., Cai, B., Hu, W., Jia, B., Liang, F., Wilson, T.W., Stephen, J.M., Calhoun, V.D. and Wang,Y.P., 2019. Joint Bayesian-incorporating estimation of multiple Gaussian graphical models tostudy brain connectivity development in adolescence. IEEE transactions on medical imaging,39(2), 357-365.
Zhang, G., Cai, B., Zhang, A., Stephen, J.M., Wilson, T.W., Calhoun, V.D. and Wang, Y.P., 2019. Estimating dynamic functional brain connectivity with a sparse hidden Markov model. IEEEtransactions on medical imaging, 39(2), 488-498.
Zhang, A., Fang, J., Liang, F., Calhoun, V.D. and Wang, Y.P., 2018. Aberrant Brain Connectivity inSchizophrenia Detected via a Fast Gaussian Graphical Model. IEEE journal of biomedical andhealth informatics, 23(4), pp.1479-1489.