Assistant Professor of Statistics
  • 356 Hanes Hall, Department of Statistics and Operations Research, UNC Chapel Hill
  • zhengwu_zhang@unc.edu

Short Bio

Zhengwu Zhang is an assistant professor in Statistics and Operations Research at UNC Chapel Hill. He was an assistant professor in Biostatistics and Computational Biology and Neuroscience at the University of Rochester from 2017 to 2020. Before his tenure-track positions, he was a postdoctoral fellow at the Statistical and Applied Mathematical Sciences Institute (SAMSI) and Duke University. He got his Ph.D. in Statistics from the Florida State University in May 2015 under Professor Anuj Srivastava's supervision.

His 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.

Most of his recent research focuses on developing novel machine learning methods to extract knowledge from large neuroimaging datasets. With advancements of in-vivo brain imaging techniques, large-scale neuroimaging datasets containing more than 10k subjects can be easily accessed now. With large samples, we can gain more statistical power, a narrower margin of error, and reproducible results, but we also face modeling and computational challenges. He is dedicated to discovering efficient, elegant, and practical solutions to these challenges.


Zhang's recent research is mainly funded by NIH MH118927 (CRCNS: Geometry-based Brain Connectome Analysis) and AG066970 (Advancing methods for structural connectome acquisition and estimation in older adults). His recent research projects include:

Optimized dMRI Data Acquisiton

2018 - Present

Connectome Extraction Pipeline Development

2015 - Present

Network Embedding and Analysis

2017 - Present

Analysis of Longitudinal Trajectories on Mainfold

2014 - Present


For a complete list of publications refer to Zhang's CV or Google Scholar page. Codes/Pipelines can be found in his GitHub website.

L. Wang, Z. Zhang. [2021]. Classification of longitudinal brain networks with an application to understanding superior aging. Stat. [in press].
M. Cole, K. Murray, E. St-Onge, B. Risk, J. Zhong, G. Schifitto, M. Descoteaux, Z. Zhang. [2021]. Surface-Based Connectivity Integration: An Atlas-Free Approach to Jointly Study Functional and Structural Connectivity. Human Brain Mapping. [in press].
B. Risk, R. Murden, J. Wu, M. Nebel, A. Venkataraman, Z. Zhang, D. Qiu. [2021]. Which Multiband Factor Should You Choose for Your Resting-State fMRI Study? NeuroImage. 234, 117965.
Z. Zhang, X. Wang, L. Kong, H. Zhu. [2021]. High-Dimensional Spatial Quantile Function-on-Scalar Regression. Journal of the American Statistical Association. [in press].
L. Wang, F. Lin, M. Cole, Z. Zhang. [2021]. Learning Clique Subgraphs in Structural Brain Network Classification with Application to Crystallized Cognition. NeuroImage. 225, 117493.
X. Wang, G. Zhu, J. Rhen, J. Pang, Z. Zhang. [2021]. Vessel Tech: A High-Accuracy Pipeline for Comprehensive Mouse Retinal Vasculature Characterization. Angiogenesis. 24, 7–11.
M. Dai, Z. Zhang, A. Srivastava. [2019]. Analyzing Dynamical Brain Functional Connectivity As Trajectories on Space of Covariance Matrices. IEEE Transactions on Medical Imaging. 39.3, 611-620.
M. Dai, Z. Zhang, A. Srivastava. [2019]. Discovering Common Change-Point Patterns in Functional Connectivity Across Population. Medical Imaging Analysis. 58, 101532.
Z. Zhang G. Allen, H. Zhu, D. Dunson. [2019]. Tensor Network Factorizations: Relationships Between Brain Structural Connectomes and Traits. NeuroImage. 197, 330-343.
L. Wang, Z. Zhang, D. Dunson. [2019]. Symmetric Bilinear Regression for Signal Subgraph Estimation. IEEE Transactions on Signal Processing. 67.7, 1929-1940.
L. Wang, Z. Zhang, D. Dunson. [2019]. Common and Individual Structure of Brain Networks. Annals of Applied Statistics. 13.1, 85-112.
Z. Zhang, E. Klassen, A. Srivastava. [2019]. Robust Comparison of Kernel Densities on Spherical Domains. Sankhya A. 81.1,144-171.
Z. Zhang, M. Descoteaux, D. Dunson. [2019]. Nonparametric Bayes Models of Fiber Curves Connecting Brain Regions.. Journal of the American Statistical Association. 114:528, 1505-1517.
Z. Zhang, J. Su, H. Le, E. Klassen, A. Srivastava. [2018]. Rate-Invariant Analysis of Covariance Trajectories. Journal of Mathematical Imaging and Vision. 60, 1306-1323.
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. 27.1, 85-97.
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]. Gaussian Blurring-Invariant Comparison 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. International Conference on Computer Vision (ICCV). Barcelona, Spain.


UNC Chapel Hill

University of Rochester
  • Fall 2020, BST 430 Intro to Statistical Computing
  • Fall 2019, BST 430 Intro to Statistical Computing
  • Fall 2018, BST 430 Intro to Statistical Computing

Awards & Grants

  • 2020-2022 "Advancing Methods for Structural Connectome Acquisition." NIH/NIA R21, PI: Zhang, Lin, Risk.
  • 2018-2021 "CRCNS: Geometry-based Brain Connectome Analysis." NIH/NIMH R01, PI: Dunson, Zhang.
  • 2018-2019 "Supernormal Structural Connectomes: Lessons for Alzheimer's Disease." Roberta K. Courtman Revocable Trust, PI: Baran, Zhang.
  • 2019-2020 "Understanding Effects of Substance Use on Brain Structural Connectome and Cognition Development during Adolescence." Health Sciences Center for Computational Innovation, NY, PI: Zhang.
  • 2018-2019 "Personalized Medical Image Analysis Based on Partial Differential Equations." UR-CTSI Pilot Grant, PI: Zhang, Qiu.
  • 2015 R.A. Bradley Award, for best Ph.D dissertation in the Department of Statistics, Florida State University.
  • 2015 CVPR 2015 Doctoral Consortium Travel Award.
  • 2015 Yongyuan and Anna Li Award for best graduate student presentations in the Department of Statistics, Florida State University
  • 2015 Graduate Student Research and Creativity Award , Only two awardees selected from STEM areas per year, Florida State University
  • 2014 Boyd Harshbarger Student Travel Award, Summer Research Conference, Galveston, Texas.
  • 2012 Brumback Award for best student presentations at Florida Chapter ASA Meeting
  • 2011 Best First Year Student in Theoretical Statistics, Florida State University