- Ph.D., Biomedical Engineering, University of Delaware, 2022
- B.S., Biomedical Engineering and Mechanical Engineering, Worcester Polytechnic Institute, 2017
- Dissertation Fellowship Award, University of Delaware Graduate College, 2021-2022
- Best Paper Award, University of Delaware Biomedical Engineering Department, 2021
- Excellence in Graduate Student Teaching, University of Delaware Faculty Senate, 2019
- Biomedical Engineering Society (BMES)
- International Society for Magnetic Resonance in Medicine (ISMRM)
- Order of the Engineer
- Society for Neuroscience (SfN)
Dr. Delgorio's background is in biomedical engineering, with a focus on neuroimaging and magnetic resonance imaging. Her expertise includes applying MR imaging to analyze and measure soft tissue viscoelasticity changes in aging and disease.
Dr. Delgorio has experience in clinical research using MR imaging sequences and applying finite element modeling tools to estimate soft tissue viscoelasticity. She also has skills in MATLAB, FMRIB imaging software library, and SPSS/R statistical software packages. Furthermore, Dr. Delgorio has prior experience using 3D CAD modeling software (SolidWorks) and performing mechanical and material testing analyses (INSTRON 5544).
Prior to joining Exponent, Dr. Delgorio obtained her Ph.D. in Biomedical Engineering from the University of Delaware where she focused on using magnetic resonance elastography, a non-invasive MR imaging technique, to capture the viscoelastic property changes of biological brain tissue in both aging and neurodegeneration. She tailored and adapted the first high-resolution MR elastography imaging protocol to reliably capture the mechanical properties of the hippocampal subfields, small brain regions important for memory processing and formation, which are affected early in aging and neurodegeneration. Through her thesis work, she gained skills in experimental design for biomedical applications in MR imaging and MR elastography, MR imaging data processing analysis, and advanced statistical models.