I am a passionate supporter of open science, contribute to and improving existing packages, and develop new libraries and tools when needed. Fork me on github.

You may be interested in the following software I develop, that span the following fields: machine learning, medical image processing, quality control, data visualization and research data management.

  • neuropredict : easy and comprehensive analysis of predictive power of biomarkers
  • visualqc: easy and rigorous quality control for neuroimaging data
    VisualQC flyer comprehensive1
  • graynet : helps extract individualized single-subject networks (pairwise links between ROIs) from T1w MRI features such as cortical thickness, gray matter density, subcortical morphometric features, gyrification and curvature.

  • Pyradigm: This is a Python class defining a machine learning dataset to ensure key-based correspondence within samples and maintaining integrity across samples. neuropredict leverages this package to a great effect. [repo]
  • Kernel methods : Comprehensive kernel methods library for advanced machine learning applications in neuroscience [repo]

  • Missing Data : visualization library for missing data, providing the comprehensive blackholes plot, summarizing the frequency of missingness across both variables as well as subjects. [repo]

  • mrivis: visualization library to build advance medical image visualizations, such as the Carpet plot (to summarize 4D or higher-dimensional images, seen below for an fMRI scan), based on compositional classes like SlicePicker and Collage to enable building of heavily customized and advanced visualizations. [repo]

    As well as variety of options to check the accuracy of alignment (or registration) between images from same modality (unimodal) or different modalities (cross-modal):

  • hiwenet: Histogram-weighted Networks for Feature Extraction, Connectivity and Advanced Analysis in Neuroscience