I'm a data scientist in Carmel Lab at Columbia University. My work focuses on developing Bayesian hierarchical models to model uncertainty and incorporate prior knowledge into analysis of sparse electrophysiological data, with the goal of better understanding movement recovery mechanisms. 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 stochastic modeling and neuroscience. I maintain a blog where I share resources and insights from my daily 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, adaptive construction of MEP recruitment curves, with the goal of optimizing experimental design by 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]