I am a data scientist in the Movement Recovery Lab at Columbia University, where my work focuses on developing Bayesian hierarchical models for multi-level, repeated measures neurophysiological data that are often sparse, with the goal of better understanding movement recovery mechanisms. More recently, I am also working on designing adaptive methods to improve experimental design. I received my MS in Mathematics from IIT Kanpur (2021) and MS in Data Science from Columbia University (2022), and have since worked at the intersection of mathematics, statistics, and neuroscience. I also maintain a blog where I share resources I find useful in my work.
Tyagi, V., Murray, L. M., Asan, A. S., Mandigo, C., Virk, M. S., Harel, N. Y., Carmel, J. B., & McIntosh, J. R. (2025). Hierarchical Bayesian estimation of motor-evoked potential recruitment curves yields accurate and robust estimates. Brain Stimulation. [DOI] [PDF]
Python library for hierarchical Bayesian estimation of motor-evoked potential (MEP) size recruitment curves. [GitHub] [Docs]
Carmel Lab awarded NIH R03 grant to extend hbMEP for real-time application, involving adaptive estimation of MEP recruitment curves, with the goal of reducing the number of stimuli and shortening session duration. [Details]
Gave a talk titled “Hierarchical Bayesian estimation of motor-evoked potential recruitment curves yields accurate and robust estimates” at the Society for Neuroscience (SfN) Nanosymposium on Analytical Computational Tools, Chicago. [Details]
Received poster award at the 11th Annual Minnesota Neuromodulation Symposium for the presentation “Motor evoked potential recruitment curve analysis using hierarchical Bayesian modeling yields accurate and robust estimates.” [View award]
Carmel Lab awarded $1.25M CDMRP grant (3 years) to study spinal cord stimulation for restoring arm and hand function after injury. hbMEP will support analysis and optimization of stimulation parameters, including electrode position, orientation, and size. [Details]