Rate My Professor Yuanyuan Gao

Gao's research specializations include neuroimaging, computational neuroimaging, clinical neuroimaging, optical neuroimaging, functional near-infrared spectroscopy (fNIRS), optical simulation, neuromodulation, human motor learning, motor function recovery, machine and deep learning, and biostatistics. She has authored key publications such as "Deep learning-based motion artifact removal in functional near-infrared spectroscopy" (Neurophotonics, 2022), "A machine learning approach to predict surgical learning curves" (Surgery, 2020), "Functional brain imaging reliably predicts bimanual motor skill performance in a standardized surgical task" (IEEE Transactions on Biomedical Engineering, 2021), "Short-separation regression incorporated diffuse optical tomography image reconstruction modeling for high-density functional near-infrared spectroscopy" (Neurophotonics, 2023), and "Decreasing the surgical errors by neurostimulation of primary motor cortex and the associated brain activation via neuroimaging" (Frontiers in Neuroscience, 2021). Her scholarship has accumulated over 385 citations on Google Scholar, impacting fields like brain imaging and surgical training. Gao has delivered public lectures at Wichita State University, including on functional neuroimaging and study design.