Academic Credentials
  • Ph.D., Mechanical Engineering, University of Illinois at Urbana-Champaign, 2023
  • M.S., Mechanical Engineering, University of Illinois at Urbana-Champaign, 2019
  • B.S., Bioengineering, University of Illinois at Urbana-Champaign, 2014
Academic Appointments
  • Graduate Research Assistant, Tissue Biomechanics Lab, University of Illinois Urbana-Champaign, 2017 – 2023
  • Graduate Teaching Assistant, Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign, 2017 – 2018
Professional Honors
  • PhD-level Student Paper Competition Winner, ASME-BED/SB3C, 2022
  • Graduate fellow, Beckman Institute for Advanced Science and Technology, 2020
Professional Affiliations
  • American Society of Biomechanics (member)
  • Equine Science Society (member)
  • Order of the Engineer

Ms. Moshage specializes in the characterization of tissue biomechanics through a combination of imaging, experimental, and computational techniques. Her formal training in bioengineering and mechanical engineering includes quantitative image analysis, using clinical computed tomography (CT) and microCT, mechanical testing, motion capture of large animals, and creation of subject-specific finite element models from imaging data.

Prior to joining Exponent, Ms. Moshage received her Ph.D. training in Mechanical Engineering from the University of Illinois Urbana-Champaign. While there, she studied the adaptation of juvenile equine bone in response to early age exercise interventions, with the goal of reducing fractures in adult racehorses. She developed the first empirical relationship between clinical CT density and compressive modulus for juvenile equine bone, which she then applied to determine subject-specific adaptations to exercise in a group of young horses. Additionally, she quantified the relationship between microstructure and mechanical properties in juvenile equine trabecular bone. This work required extensive machining of bone tissue samples, design of mechanical test fixtures, scanning in both clinical CT and microCT, and coding in MATLAB to automate all processing steps for image analysis and mechanical test data.