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saurabh vyas

graduate student at stanford university

about me

How does the brain prepare and execute movements from as simple as reaching for a cup of coffee to performing a perfectly synchronized dive? Which computational principles does the brain use to help us learn new motor skills? Do these insights enable development of next-generation medical devices that can help people with movement disorders? To study these questions, I apply techniques from statistical signal processing, machine learning, and dynamical system theory to uncover computational motifs that underlie motor control and learning.

career highlights

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I'm a Ph.D. candidate advised by Krishna Shenoy in the Neural Prosthetic Systems Lab. My research consists of two broad goals:

  1. To investigate how the coordination amongst large populations of neurons gives rise to movement and supports learning new motor skills.
  2. To apply these insights to develop high-performance brain-machine interfaces, which in and of themselves support study of basic motor neuroscience.

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Before coming to Stanford I was a research engineer at the Johns Hopkins University Applied Physics Laboratory, where I developed machine learning and computer vision algorithms for a variety of applications including robotics, medical image analysis, biophysical modeling, and remote sensing.

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I completed my undergraduate work at Johns Hopkins University, where I developed neural signal processing algorithms for studying Parkinson's disease and epilepsy, as well as novel computer vision algorithms for medical robotics applications.