Academic Credentials
  • Ph.D., Biomedical Engineering, University of Florida, 2023
  • B.S., Mechanical Engineering, University of South Florida, 2018
Professional Honors
  • NSF Graduate Research Fellow (2019-2023)
  • UF Graduate Student Preeminence Award (2018-2023)
  • UF Institute for Computational Engineering Fellow (2018)
Professional Affiliations
  • American Society of Biomechanics, 2018-2023
  • Tau Beta Pi, 2015-2018
  • Phi Theta Kappa, 2014-2015

Dr. Kearney has expert knowledge of human movement analysis, with an emphasis on artificial intelligence/machine learning (AI/ML) and simulation techniques. She has developed and validated traditional (e.g., random forests and support vector machines) and deep (e.g., feedforward and long-short term memory neural networks) models of human movement. She has also employed simulation tools in OpenSim (e.g., inverse/forward dynamics, direct collocation, and computed muscle control) to model a range of biomechanical systems. To train and validate her models, she has collected human-participant data using techniques that include electromyography (fine-wire and surface), dynamometric measurements, 3D motion capture, and ultrasound.

Prior to joining Exponent, Dr. Kearney was a graduate researcher for the Musculoskeletal Biomechanics Laboratory at the University of Florida. Her doctoral research focused on the prediction and characterization of upper extremity biomechanics. She developed and validated a simulation-to-real transfer learning approach for the prediction of upper extremity forces and torques in healthy young adults. She employed custom deep-learning pipelines and explainable AI techniques to enhance the transparency and interpretability of her models and their predictions. Her experience also extends to clinical applications, such as the employment of unsupervised techniques to characterize carpometacarpal osteoarthritis expression.

As a graduate researcher, Dr. Kearney completed an internship with the Honda Research Institute (HRI) in Offenbach, Germany. At HRI, she applied her knowledge of ML, musculoskeletal simulations, and human-participant data collections to progress HRI's goals of designing robotic systems capable of cooperative intelligence.