ZHENGWU ZHANG

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 currently serves as an Associate Editor for Reproducibility at Journal of the American Statistical Association. 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.

Research

Zhang's recent research is mainly funded by NIH MH118927 (CRCNS: Geometry-based Brain Connectome Analysis), AG066970 (Advancing methods for structural connectome acquisition and estimation in older adults), R21AG073356 (ANS-based Personalized Cognitive Training) and R25DA058940 (Promoting Collaborative Research on Human Connectome Analysis for Substance Use Disorders). His recent research projects include:

UNC Education Program of Intelligence and Connectomics (EPIC)

The EPIC is an interdisciplinary eduction program offering brief courses and practical workshops in brain network analysis. Its aim is to involve university students and prepare new researchers with the computational abilities to address mental health issues in practical settings. See more details on our EPIC website.

2023 - Present

Optimized Diffusion MRI Data Acquisiton

2018 - Present

Brain Connectome Representation and Modeling

I maintain a GitHub website named "Surface-Based Connectivity Integration (SBCI) for the Human Brain". This website contains pipelines for extracting brain structural and functional networks from raw MRI data, visualization, and statistical analysis code for SBCI brain networks, along with several SBCI datasets.

2015 - Present

Network Data Analysis

2017 - Present

Analysis of Longitudinal Trajectories on Mainfold

2014 - Present

Publications

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

Y. Zhang, M. Liu, Z. Zhang, D. Dunson [2024]. Motion-Invariant Variational Auto-Encoding of Brain Structural Connectomes. Imaging Neuroscience.
A. Allen, Z. Zhang, A Nobel [2024]. CoCoNest: A Continuous Structural Connectivity-based Nested Parcellation of the Human Cerebral Cortex. Network Neuroscience .
W. Consagra, M. Cole, X. Qiu and Z. Zhang [2024]. Continuous and Atlas-free Analysis of Brain Structural Connectivity. Annals of Applied Statistics.
L. L. Duan, Z. Yuwen, G. Michailidis, Z. Zhang [2023]. Low Tree-Rank Bayesian Vector Autoregression Models. Journal of Machine Learning Research.
D. Li, P. Nguyen, Z. Zhang, D. Dunson [2023]. Tree Representations of Brain Structural Connectivity via Persistent Homology. Frontiers in Neuroscience, Brain Imaging Methods.
Y. Zhao, C. Chang, J. Zhang, Z. Zhang [2023]. Genetic underpinnings of brain structural connectome for young adults. Journal of the American Statistical Association.
Y. Li, G. Mateos, Z. Zhang [2022]. Learning to Model the Relationship Between Brain Structural and Functional Connectomes. IEEE Transactions on Signal and Information Processing over Network.
Z. Zhang, Y. Wu, D. Xiong, J. G. Ibrahim, A. Srivastava, H. Zhu. [2022]. LESA: Longitudinal Elastic Shape Analysis of Brain Subcortical Structures. Journal of the American Statistical Association, accepted as a discussion paper.
P. Dey, Z. Zhang, D. Dunson. [2022]. Outlier Detection for Multi-Network Data. Bioinformatics.
Z. Zhang, B. Saparbayeva. [2022]. Amplitude Mean of Functional Data on S2 and its Accurate Computation. Journal of Mathematical Imaging and Vision.
Q. Chen, A. Turnbull, M. Cole, Z. Zhang, F. V. Lin. [2022]. Enhancing Cortical Network-Level participation Coefficient as a Potential Mechanism for Transfer in Cognitive Training in aMCI. NeuroImage.
W. Consagra, A. Venkataramana, Z. Zhang. [2022]. Optimized Diffusion Imaging for Brain Structural Connectome Analysis. IEEE Transactions on Medical Imaging, an earlier version won the runner-up award in the paper competition sponsored by the ASA Statistical Methods in Imaging Section.
Z. Zhang, J. Gewandter, P. Geha. [2021]. Brain Imaging Biomarkers for Chronic Pain. Frontiers in Neurology.
M. Liu Z. Zhang, D. Dunson. [2021]. Graph Auto-Encoding Brain Networks with Applications to Analyzing Large-Scale Brain Imaging Datasets. NeuroImage. 245, 118750.
G. Papadogeorgou Z. Zhang, D. Dunson. [2021]. Soft Tensor Regression. Journal of Machine Learning Research. 22, 1-53.
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.

Teaching

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

  • 2022 UNC Chapel Hill Junior Faculty Development Award.
  • 2022 Oak Ridge Associated Universities Ralph E. Powe Junior Faculty Enhancement Award.
  • 2022 Discussion paper in Journal of the American Statistical Association (Applications & Case Studies).
  • 2022 Supervised paper won runner-up award in the paper competition sponsored by the ASA Statistical Methods in Imaging Section. Paper title "Optimized Diffusion Imaging for Brain Structural Connectome Analysis".
  • 2021-2026 "Develop an ANS-based Personalized Cognitive Training for Mild Cognitive Impairment." NIH/NIA R21/R33, PI: Lin, Zhang, Tapparello.
  • 2020-2023 "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