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saurabh vyas

postdoctoral scientist at columbia university

about me

The computations performed by the brain are exceedingly remarkable - one need look no further than the seemingly impossible movements performed by Olympic athletes. However, even simple movements - like reaching for your morning cup of coffee - require a slew of brain regions to rapidly coordinate and generate highly complex patterns of activity that ultimately move your arm. The richness in this activity endows our nervous system with the ability to not only learn how to, for example, run along a balance beam, but also make split-second mid-air decisions to stick that landing. I'm a neuroscientist who studies the computational and algorithmic principles surrounding such neural control of movement.

career highlights

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I'm currently a postdoctoral research scientist in the Mortimer B. Zuckerman Mind Brain Behavior Institute at Columbia University working with Mark Churchland. My research on motor control broadly leverages a computation through neural population dynamics framework, which I describe in detail in this review (Vyas et al., Annual Review of Neuroscience, 2020).

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I completed my Ph.D. at Stanford University advised by Krishna Shenoy in the Neural Prosthetic Systems Lab. My work revealed that neural activity in the motor cortex before the onset of movement, and even in the absence of movement altogether, could play a fundamental role in the algorithms underlying motor learning (e.g., Vyas et al., Neuron, 2018; Vyas et al. Neuron, 2020).
In 2021, my thesis was awarded The Donald B. Lindsley Prize in Behavioral Neuroscience by The Grass Foundation and The Society for Neuroscience (this award recognizes an outstanding Ph.D. thesis in the general area of behavioral neuroscience).

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After college, 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 (e.g., Vyas et al., Journal of biomedical optics, 2013).

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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 (e.g., Vyas et al., IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015).