In my research, I am excited about developing machine learning tools specifically designed to solve problems within neuroscience. I am currently a postdoctoral researcher at Columbia University in the Center for Theoretical Neuroscience, working with Liam Paninski and John Cunningham. I completed my Ph.D. in neuroscience at Northwestern University in the lab of Konrad Kording. Before that, I was an undergrad at the University of Illinois Urbana-Champaign, studying physics and math. I look forward to helping mentor the next generation of computational neuroscientists.
Principal Investigator: Liam Paninski
Fellow: Nana Dufie Akowuah
Developing neural “decoders” (mappings from neural activity to motor output) that are accurate across many types of movements is a central challenge in neural engineering and motor control. Overcoming this challenge is of great importance to brain computer interfaces (BCIs), which could help restore movement to those with spinal cord injuries, stroke, and more. For BCIs to be widely applicable, it is essential that they can “generalize” to new movements that the BCI decoder was not explicitly trained on. For example, if we train a decoder using slow movements, we would still want it to work when making fast movements. In this project, we will work to develop machine learning decoders that are better able to generalize to new conditions. To do so, we will fit decoding models to collaborators’ data that includes simultaneous neural and muscle recordings. The ability to code in Python will be beneficial for this project.