Nilearn is a Python package for fast and easy statistical learning on NeuroImaging data with a focus on fMRI data. It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.
Open source contributionsThis page presents the main open source projects (softwares, datasets...) I have contributed to. For a more accurate overview of my contributions, please have a look at my github profile.
StreamGraphs.jl is a Julia package to work with stream graphs and link streams. I am currently working on its development at LIP6. Feel free to drop me an email if you are interested in contributing!
Large-scale synthetic distribution and sub-transmission dataset based on building and streetmap data for Santa Fe, New-Mexico, USA. (Not the real network) Produced using RNM-US as part of the NREL-MIT-Comillas-CYME-EDD Smart-DS Arpa-e project.
DiTTo aims at providing an open source framework to convert various distribution system modeling formats. It is the first, and currently only open source, tool to provide these capabilities.
Dynamo is a modular MATLAB toolkit for Dynamic programming (DP) and Approximate Dynamic Programming (ADP) for Adaptive Modeling and Optimization.
The Power Grid Dataset is a free dataset of real power grid system topologies. It was developed at Telecom SuParis in 2017 with Vincent Gauthier and Lester Padilla.