Sebastian (Sebo) Diaz

Sebastian (Sebo) Diaz is a PhD candidate in medical engineering and medical physics whose research interests are focused on developing innovative techniques for fetal imaging and addressing the challenges of imaging in pregnancy. Specifically, Sebo is working on new motion-compensation techniques and a fetal motion analysis framework, both of which draw extensively on MATLAB. With the support of a MathWorks Fellowship, he will pursue several objectives, including the development of a data-driven deep-learning framework that will ultimately provide motion-compensated, high-quality metabolic spectra for clinical use. He will also utilize sophisticated MRI pulse sequences and deep learning to create labels for various parts of the fetal body, enabling tracking and analysis of fetal movement over time. Finally, he is developing a new technique for examining neurological progression using time-series key point estimates to compare fetal motion at a particular gestational age to motion parameters known to align with healthy progression. Sebo’s research could enable more accurate fetal imaging and more detailed characterizations of neurodevelopmental complications, providing clinicians with important tools and information to treat and care for the growing baby.