Cary, North Carolina, United States
My name is Yousif Shwetar, and I am an MD-PhD student at the University of North Carolina at Chapel Hill. My research interests broadly involves the application of various machine learning techniques to the field of medical imaging and devices. My current doctorate work involves leveraging wearable devices for early disease detection and utilizing imaging technologies to automate diagnostic testing, pertinently in the field of ophthalmic diseases. As of 2020 I'm an alumni of the University of Pittsburgh with an Honors degree in Biomedical Engineering. During my time, I was awarded the Cathedral Achievement Scholar and placement on the Swanson School of Engineering deans list.
Medical Scientist Training Program is an NIH funded dual degree program that has students pursue both a doctorate of medicine and doctorate of philosophy in their desired field of research with the goal of bridging the gap between medical research and clinical application.
Primarily focused on developing code to to track various physical activity parameters in the physically disabled population, most specifically manual wheelchair users. Collaborated with physicians and scientists in VA funded research projects. Other responsibilities of the position included recruiting participants, data collection, recording of physiological baseline characteristics, quantitative and qualitative assessment of data, and scientific manuscript writing. By being incorporated in all phases of the scientific research process, this position has set the basis for my research and development skills. We published a number of articles in the field as well as my own thesis, all of which can be found on my google scholar.
Under the supervision of Marc Simon and researchers, I investigated the ability for clinicians and engineers to predict right sided heart failure post LVAD implant. We investigated various physiological variables derived from right heart characterizations, with dP/dt showing most potential for use in future studies. I shared my findings at the 2017 Biomedical Engineering Society conference in Phoenix Arizona, with my work titled as "Assessment of Patient Hemodynamics Pre-Left Ventricle Assist Device Implant to Determine Chance of Right Ventricular Failure".