Sarah Divel

Sarah Divel has a diverse work experience in the field of medical imaging and technology. Sarah currently works as a Senior Innovation Engineer at RapidAI since May 2022. Prior to this, they were a Project Manager in Product Validation at RapidAI from July 2021 to May 2022. Sarah also worked as a Senior ML Engineer in Clinical Validation at RapidAI from October 2020 to July 2021.

Before joining RapidAI, Sarah had a research-focused position as a Postdoctoral Scholar at Stanford University in September 2020. Sarah also worked as a Graduate Research Assistant at Stanford University from January 2015 to August 2020, where they developed a framework for simulating and evaluating CT perfusion studies for stroke assessment. Additionally, Sarah served as a Course Assistant for the Biochips and Medical Imaging course at Stanford University in January 2017 to March 2017.

Sarah gained valuable experience as an ORISE Fellow at FDA's Center for Devices and Radiological Health in June 2018 to August 2018. During this role, they implemented and evaluated convolutional neural networks for low-dose CT image denoising.

Early in their career, Sarah interned as a CT Systems Engineering intern at GE Healthcare in the summers of 2012 and 2013. In these roles, they contributed to the development and testing of new technologies for CT scanners.

Sarah's work experience highlights their expertise in medical imaging research, project management, machine learning, and technological innovation.

Sarah Divel earned a Bachelor of Science (B.S.) degree in Electrical Engineering from the University of Notre Dame between 2010 and 2014. Sarah then pursued further education at Stanford University, completing a Master of Science (M.S.) degree in Electrical Engineering from 2014 to 2016. Sarah continued their studies at Stanford, where they obtained a Doctor of Philosophy (Ph.D.) degree in Electrical Engineering between 2014 and 2020.