Jennifer A Nichols,

Dr. Nichols is an Associate Professor in the J. Crayton Pruitt Family Department of Biomedical Engineering at the University of Florida with Affiliate Faculty Appointments in the Department of Mechanical & Aerospace Engineering, the Department of Orthopaedic Surgery & Sports Medicine, and the Department of Physical Medicine & Rehabilitation. She received a B.S. in Mechanical Engineering from Tufts University in 2008 and completed graduate training at Northwestern University, where she received an M.S. in Biomedical Engineering in 2011, an M.A. in Medical Humanities & Bioethics in 2014, and a Ph.D. in Biomedical Engineering in 2014. From 2014 to 2017, she completed a postdoctoral fellowship in the Department of Orthopaedics at the University of Utah. At the University of Florida, she directs the Musculoskeletal Biomechanics Lab. Her research interests include biomechanics, musculoskeletal modeling, complex joint systems such as the foot/ankle and wrist/hand, predictive surgical simulations, osteoarthritis, musculoskeletal pain, quantitative medical imaging, machine learning, and research ethics.

Dr. Nichols’ research broadly focuses on musculoskeletal biomechanics. The guiding vision of her lab is to create predictive simulations that cross disciplinary boundaries and improve the functional ability and quality of life for individuals with musculoskeletal disorders. In other words, she is actively working to create a future in which advanced computer simulations enable real-time, patient-specific prognostics in a doctor’s office. Toward this goal, her lab is pursuing (i) basic science projects focused on understanding musculoskeletal structure and function, (ii) computationally intensive projects to rapidly create personalized models and analyze predictive patient-specific simulations, and (iii) clinical projects focused on improving orthopaedic surgery and rehabilitation. All these projects integrate orthopaedic biomechanics, musculoskeletal computer simulations, quantitative medical imaging, and machine learning to understand both the patient- and surgeon-specific factors that influence musculoskeletal health.