Amol Pasarkar

Ph.D Candidate

New York, New York, United States
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Summary

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Rockstar
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Top School
Amol Pasarkar is a Ph.D. candidate at Columbia University with 10 years of experience building machine learning methods for neural data under the guidance of Liam Paninski. His background blends rigorous theoretical work—early research published in Innovations in Theoretical Computer Science with advisors Christos Papadimitriou and Mihalis Yannakakis—with hands-on engineering internships at Oak Ridge National Laboratory and PlaceIQ. Trained in computer science at Columbia, he focuses on translating statistical and computational theory into practical tools for neuroscience data analysis. Comfortable in both academic and applied settings, he brings experience across research, software engineering, and geospatial data from multidisciplinary teams. A detail that sets him apart is his trajectory from influential theoretical undergraduate research to targeted ML methods for neural datasets, signaling strength in both abstractions and real-world implementation.
code10 years of coding experience
job2 years of employment as a software developer
bookBachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at Columbia University in the City of New York
bookFair Lawn High School
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Github Skills (39)

visualization10
scientific-visualization10
matplotlib9
vulkan9
gpu9
metal9
python8
automatic-differentiation8
webgpu8
pytorch7
autodiff7
machine-learning7
segmentation7
composable7
jit7

Programming languages (3)

C++Jupyter NotebookPython

Github contributions (5)

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apasarkar/rlocalnmf

Jul 2022 - Jan 2025

New localNMF implementation with advanced background models and initialization options
Contributions:16 PRs, 130 pushes, 16 branches in 2 years 7 months
optionsbackground
apasarkar/localmd

Sep 2022 - Feb 2025

Accelerated local matrix decomposition for calcium imaging data
Contributions:25 PRs, 113 pushes, 22 branches in 2 years 5 months
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Amol Pasarkar - Ph.D Candidate