Athena Taymourtash is a School of Engineering Distinguished Postdoctoral Fellow who develops machine learning methods for optimized acquisition and robust analysis of medical images. Her research interests lie at the intersection of computer science and neuroimaging, with a specific emphasis on computational modeling, computer vision, and machine learning applied to prenatal imaging and healthcare challenges. In her doctoral research, Athena developed novel algorithms for fetal magnetic resonance imaging (MRI) analysis, particularly in motion tracking, super-resolution, and real-time correction to improve diagnostic imaging. This postdoctoral fellowship will enable her to expand her efforts in advancing fetal MRI technology by exploring new machine learning approaches to enhance image quality, reduce scan times, and improve diagnostic accuracy. Athena’s work has the potential to bridge cutting-edge computational techniques with clinical applications, contributing to improved outcomes in maternal-fetal healthcare, and at a broader level to expand the use of innovative machine learning approaches in healthcare.