This research develops advanced brain-machine interface systems to improve life for spinal cord injury patients. Using neural networks such as FinNet and dynamic recurrent neural decoders, the work aims to better extract and translate brain activity into movement while creating low-power hardware capable of supporting long-term practical neuroprosthetic applications.

This research examines how “sitting is the new smoking” headlines affect people with spinal cord injury. Interviews revealed these messages are harmful and exclusionary. Reframing sedentary behavior as low energy expenditure, rather than sitting itself, improves understanding. The work promotes inclusive, evidence-based public health communication.

This research explores neural remodeling—the process by which neurons form new connections after spinal cord injury. Using mouse models, the work identifies genes involved in detour pathways and enhances them through gene therapy, strengthening recovery. The goal is to develop future treatments that improve functional outcomes for people with central nervous system injuries.