Zhengwu Zhang, PhD
Assistant Professor of Biostatistics and Computational Biology
Assistant Professor of Department of Neuroscience - Joint
Ph.D. (2015) Florida State University
University of Rochester
Dept of Biostatistics and Computational Biology
265 Crittenden Boulevard, CU 420630
Rochester, New York 14642-0630
Office: Saunders Research Building 4169
Phone: (585) 276-6588
Fax: (585) 273-1031
My primary research interests lie in developing effective statistical and machine learning methods for high-dimensional ``objects'' with low-dimensional underlying structures. Examples of these objects include images, surfaces, networks, and time-indexed paths on non-linear manifolds, coming from neuroscience, computer vision, epidemiology, genomics, and meteorology.
My recent study focuses on developing novel machine learning methods to extract knowledge from large neuroimaging datasets. With the advancement of in vivo brain imaging techniques, large-scale neuroimaging datasets containing more than 1k or even 10k subjects (e.g., the Adolescent Brain Cognitive Development Study dataset, the Human Connectome Project dataset, the Alzheimer’s Disease Neuroimaging Initiative dataset, and the UK Biobank dataset) can be easily accessed now. With large samples, we gain more statistical power, narrower margin of errors and more reproducible results, but we also face modeling and computational challenges. I am dedicated to discovering efficient, elegant and practical solutions to these challenges.
Full List of Publications
A complete list of recent publications is available through my URMC Profile.
- L. Wang, Z. Zhang, D. Dunson. (2019). Symmetric Bilinear Regression for Signal Subgraph Estimation. IEEE Transition on Signal Processing, in press.
Z. Zhang, M. Descoteaux, David Dunson. (2019). Bayesian Modeling of Fiber Tracts Connecting Brain Regions. Journal of the American Statistical Association, in press.
L. Wang, Z. Zhang, D. Dunson. (2019). Common and Individual Structure of Multiple Networks. Annals of Applied Statistics, in press.
Z. Zhang, J. Su, H. Le, E. Klassen, A. Srivastava. (2018). Rate-Invariant Analysis of Covariance Trajectories. Journal of Mathematical Imaging and Vision, 1-18.
Z. Zhang, E. Klassen, A. Srivastava. (2018). Robust Comparison of Kernel Densities on Spherical Domains. Sankhya A, 1-28.
- Z. Zhang, M. Descoteaux, J. Zhang, D. Dunson, A. Srivastava, H. Zhu. (2018). Mapping Population-based Structural Connectome. NeuroImage, 172, 130-145.
Z. Zhang, E. Klassen, A. Srivastava. (2018). Phase-Amplitude Separation and Modeling of Spherical Trajectories. Journal of Computational and Graphical Statistics, 1-13.
M. Dai, Z. Zhang, A.Srivastava. (2017). Discovering Change-Point Patterns in Dynamic Functional Brain Connectivity of a Population. In International Conference on Information Processing in Medical Imaging (IPMI). pp. 361-372. Springer, Cham.
- Z. Zhang, D. Pati, A. Srivastava. (2015). Bayesian Clustering of Shapes of Curves. Journal of Statistical Planning and Inference, 166, 171-186.
- Z. Zhang, E. Klassen, A. Srivastava. (2013). Blurring-Invariant Comparisons of Signals and Images. IEEE Transactions on Image Processing, 22(8), 3145-3157.
Z. Zhang, E. Klassen, A. Srivastava, P.K. Turaga, R. Chellappa. (2011) Blurring-Invariant Riemannian Metrics for Comparing Signals and Images, in International Conference on Computer Vision (ICCV), pp. 1770-1775. Barcelona, Spain.
Last updated: February 12, 2019