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Zhengwu Zhang, PhD

Assistant Professor of Biostatistics and Computational Biology
Ph.D. (2015) Florida State University

Zhengwu ZhangContact Information

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

Research Interests

My main interests lie in developing statistical methods for high-dimensional “objects” with low-dimensional underlying structures, e.g. images, contours, surfaces, networks and time-indexed paths on non-linear manifolds. These datatypes are abundant in various fields: neuroscience, epidemiology, genomics, meteorology and computer vision. Interdisciplinary thinking across  different fields, e.g. Statistics, Computer Science, Mathematics, is the key to solve challenges raised from efficiently analyzing such "objects".

Full List of Publications

A complete list of recent publications is available through my URMC Profile.

Submitted Papers

  • M. Dai, Z. Zhang, A. Srivastava, “Discovering Common Change-Point Patterns in Functional Connectivity Across Population”, under review, 2017+
  • L. Wang, Z. Zhang, D. Dunson, “Common and Individual Structure of Multiple Networks”, under review, 2017+
  •  Z. Zhang, J. Su, H. Le, E. Klassen, A. Srivastava, “Rate-Invariant Analysis of Covariance Trajectories”, second revision at Journal of Mathematical Imaging and Vision, 2017+
  • Z. Zhang, M. Descoteaux, D. Dunson: “Bayesian Modeling of Fiber Tracts Connecting Brain Regions”, revision at Journal of the American Statistical Association, 2017+
  •  Z. Zhang, E. Klassen, A. Srivastava, “A Robust Comparison of Kernel Densities on Spherical Domains and its Application in Two-Sample Hypothesis Testing”, 2017+
  •  Z. Zhang, M. Descoteaux, J. Zhang, D. Dunson, A. Srivastava, H. Zhu: “Mapping Population-based Structural Connectome”, third revision at Neuroimage, 2017+

Peer-Reviewed Journals

  • X. Dong, Z. Zhang, A. Srivastava: “Bayesian Tractography Using Geometric Shape Priors”, to appear, Frontiers Neuroscience, 2017+
  • Z. Zhang, E. Klassen, A. Srivastava, “Phase-Amplitude Separation and Modeling of Spherical Trajectories”, to appear, Journal of Computational and Graphical Statistics, 2017+
  •  Z. Zhang, D. Pati, A. Srivastava, “Bayesian Clustering of Shapes of Curves”, Journal of Statistical Planning and Inference, Vol 166, pp 171-186, 2015.
  • Z. Zhang, E. Klassen, A. Srivastava, “Blurring-Invariant Comparisons of Signals and Images”. IEEE Transactions on Image Processing, Vol 22, No. 8, 2013.

Peer-Reviewed Conference Proceedings with Low Acceptance Rates

  • M. Dai, Z. Zhang, A.Srivastava: “Discovering Change-Point Patterns in Dynamic Functional Brain Connectivity of a Population”, Information Processing in Medical Imaging (IPMI), 2017.
  • M. Dai, Z. Zhang, A. Srivastava, “Testing Stationarity of Brain Functional Connectivity Using Change-Point Detection in fMRI Data”  Diff-CVML, CVPR, 2016.
  • C. Xu, Z. Zhang, J. Liu, X. Tang, “3D Object Search Through Semantic Component”, ACM Multimedia, 2010:959-962.
  • Z. Zhang, E. Klassen, A. Srivastava, P.K. Turaga, R. Chellappa, “Blurring-Invariant Riemannian Metrics for Comparing Signals and Images”, International Conference on Computer Vision (ICCV) 2011:1770-1775, Barcelona, Spain, 2011.

Book Chapters

  • Z. Zhang, A. Srivastava, Q. Xie, “Elastic Registration and Shape Analysis of Functional Objects”, Geometry Driven Statistics, 2015.
  • S. Joshi, J. Su, Z. Zhang, B. Amor. “Elastic Shape Analysis of Functions, Curves and Trajectories”. Riemannian Computing in Computer Vision, 2016.
  • Z. Zhang, D. Pati, A. Srivastava, “Bayesian Shape Clustering”, Nonparametric Bayesian Inference in Biostatistics, 2015.
  • A. Duncan, Z. Zhang, A. Srivastava, “An Elastic Riemannian Framework for Shape Analysis of Curves and Tree-Like Structures”, Algorithmic Advances in Riemannian Geometry and Applications, Oct. 2016.