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.
10 years of coding experience
2 years of employment as a software developer
Bachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at Columbia University in the City of New York
Accelerated local matrix decomposition for calcium imaging data
Contributions:25 PRs, 113 pushes, 22 branches in 2 years 5 months
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