Name: Pradeep Reddy Raamana
Type: User
Company: University of Pittsburgh
Bio: Neuroscientist trying to bridge the gap between clinic & computer science. Interests: Machine learning, Neuroimaging, Brain disorders, Informatics, Open science
Twitter: Raamana_
Location: Pittsburgh, PA
Blog: crossinvalidation.com
Pradeep Reddy Raamana's Projects
ACRONYM (Acronym CReatiON for You and Me)
Official AFNI source and documentation
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
Advanced Normalization Tools in Python
Automated Machine Learning with scikit-learn
accessible AutoML for deep learning.
A curated list of awesome Machine Learning frameworks, libraries and software.
Adaptive Experimentation Platform
Its like Tinder, for Brains with Lesions!
A library of sklearn compatible categorical variable encoders
A Python package for modular causal inference analysis and model evaluations
Canonical Correlation Analysis Model Zoo: Standard: CCA, GCCA, MCCA, TCCA, KCCA, TKCCA, sparse CCA , ridge CCA and elastic CCA, PMD, PLS. Deep: DCCA, DMCCA, DGCCA, DTCCA. DVCCA, DCCAE, SplitAE. Probabilistic: VBCCCA. With simulated data generation and toy datasets.
A guide to help you write better command-line programs, taking traditional UNIX principles and updating them for the modern day.
Conquering confounds and covariates: methods, library and guidance
connectome-based white matter atlases for virtual lesion studies
Cookiecutter template for a Python package.
The Python programming language
A Python-embedded modeling language for convex optimization problems.
Keep scientific data under control with git and git-annex
DICOM to Nifti conversion with meta data preservation
A Comprehensive Assessment of Trustworthiness in GPT Models
Navigate front-end codebases in Sublime Text 2/3
Facilitates DICOM data access.
Diffusion MR Imaging in Python
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
Automated collection and reporting of citations for used software/methods/datasets
A library for debugging/inspecting machine learning classifiers and explaining their predictions
Participant selection for workshops and conferences made easy