Thomas Possidente

Doctoral Candidate

Cambridge, Massachusetts, United States
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Summary

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Senior
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Top School
Thomas Possidente is a Doctoral Candidate and computational neuroscientist with nine years of interdisciplinary experience at the intersection of machine learning, neuroimaging, and bioinformatics. He has applied ML and data engineering to large-scale biological sequence datasets, time-series classification, NLP and image analysis in industry settings and now focuses on perception and attention in the Somers Lab at Boston University. Skilled in Python, MATLAB, and statistical modeling, he brings hands-on experience from Draper and Brigham and Women's Hospital to academic neuroimaging experiments. His background in cognitive science and early work building unsupervised neural network models gives him a rare blend of theory-driven modeling and production-oriented engineering. Based in Cambridge, MA, he combines rigorous experimental techniques with scalable data pipelines—evident in projects handling millions of protein sequences and diverse biomedical imaging challenges.
code10 years of coding experience
job4 years of employment as a software developer
bookBachelor’s Degree, Bachelor’s Degree at Vassar College
bookEötvös Loránd University
bookHigh School, High School at Saratoga Springs High School
languagesEnglish, Spanish
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Github Skills (10)

matlab10
r10
python9
de-identification6
bids3
datasets3
react3
neuroimaging2
neuroscience2
graphql2

Programming languages (5)

TypeScriptCJupyter NotebookMATLABPython

Github contributions (5)

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Code for EDF file deidentification in MATLAB and Python
Contributions:47 commits, 53 pushes, 1 branch in 1 year 4 months
de-identificationmatlabpython
preraulab/multitaper_toolbox

Feb 2021 - Nov 2022

A multitaper spectral estimation toolbox implemented in MATLAB, Python, and R
Contributions:68 commits, 77 pushes, 1 branch in 1 year 9 months
matlabpythonmultitaperreeg
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