Yashwanth Mogili is a Research Assistant in the Cerebrovascular & Neurointerventional Lab, where he applies machine learning and medical image analysis to stroke research. He earned his Master of Computer Science from the University of Iowa in 2026, with a focus on deep learning and biomedical image analysis. During his graduate studies, he built tools to quantify synapses in 3D confocal images as part of research on noise-induced hearing loss. He also worked with researchers at the University of Wisconsin–Madison on cell cycle research, creating a software tool that uses deep learning to automatically identify cell nuclei and classify them by cell cycle phase. In the CVNI Lab, he develops deep learning and quantitative methods that combine neuroimaging with clinical data to study stroke. He aims to use computational approaches to advance cerebrovascular research and improve outcomes for patients with stroke.