Yingying WangAssistant Professor
Ph.D., Biomedical Engineering, University of Cincinnati, 2013
M.S., Biomedical Engineering, Shanghai University, Shanghai, China, 2005
B.S., Biomedical Engineering, Shanghai University, Shanghai, China, 2002
Yingying Wang, Ph.D, is an assistant professor specializing in utilizing advance Neuroimaging techniques, such as functional Magnetic Resonance Imaging (fMRI), Diffusion Weighted Imaging (DWI), Electroencephalograms (EEGs) and Magnetoencephalography (MEG) to study brain development during literacy and language acquisition. Her passion is to be an outstanding educator and make significant research contributions in the field of brain imaging.
Yingying finished her two-year post-doctoral fellowship in Dr. Nadine Gaab's Lab at Boston Children's Hospital/Harvard Medical School in 2015. She compared brain image data from children with a family history of dyslexia with those without a family history. She found brain alterations in white matter integrity even before reading onset. With that and behavioral measures and family history, she theorizes that brain imaging data can help to identify children at risk for developing reading disabilities even before second or third grade, which opens a huge potential to implement early intervention programs to help those at-risk children to learn compensation strategies. She is also exploring the relationship between executive function and reading ability in children using fMRI with a longitudinal study design.
Yingying earned her doctoral degree in biomedical engineering under Dr. Scott K. Holland's mentorship at Cincinnati Children's Hospital/University of Cincinnati in 2013. Her dissertation, titled "Integration of fMRI and MEG towards modeling language networks in the brain", aimed to fuse fMRI and MEG data to optimize spatial and temporal resolution of imaging data. Her long-standing interest is the evolution of language network and how language network varies among individuals. By combining fMRI and MEG data using a Bayesian framework, she can tap into the dynamic changes of brain networks instead of static functional brain regions.