Dr. Zhang’s research centers on the multidisciplinary field of human brain mapping, with a focus on developing innovative statistical models and computational algorithms for analyzing multimodal brain imaging data. Her methodological expertise spans Bayesian inference, network analysis, and high-dimensional data analysis. She actively collaborates with psychologists, neurologists, and neuroscientists to advance scientific understanding of brain function and structure, with the ultimate goal of improving the diagnosis and treatment of neurological disorders.
Appointments
2022-present: Professor, Department of Statistics, University of Pittsburgh
2020-2022: Associate Professor, Department of Statistics, University of Pittsburgh
2015-2020: Associate Professor, Department of Statistics, University of Virginia
2009-2015: Assistant Professor, Department of Statistics, University of Virginia
2008-2009: Postdoctoral Fellow, Department of Statistics, Harvard University
Courses
- STAT 1231/2230 Applied Experimental Design
- STAT 1651/2650 Introduction to Bayesian Statistics
- STAT 1731/270 Stochastic Processes
- STAT 2651 Bayesian Statistics
- 2008 - Ph.D. Statistics Harvard University
- 2005 - M.S. Statistics Harvard University
- 2003 - B.S. Mathematics Peking University
Education & Training
Wang, Y., Li, S., He, J., Peng, L., Wang, Q., Zou, X., Tudorascu, D.L., Schaeffer, D.J., Schaeffer, L., Szczupak, D. and Park, J.E., 2025. Analysis of functional connectivity changes from childhood to old age: A study using HCP-D, HCP-YA, and HCP-A datasets. Imaging Neuroscience, 3, p.imag_a_00503.
Wang, Y., Yan, G., Wang, X., Li, S., Peng, L., Tudorascu, D.L. and Zhang, T., 2023. A variational Bayesian approach to identifying whole-brain directed networks with fMRI data. The Annals of Applied Statistics, 17(1), pp.518-538.
- Neuroimaging Data Analysis
- Human Brain Mapping
- Bayesian Statistics
