Year 2016 has been productive for science in my team. Here are some personal highlights: bridging artificial intelligence tools to human cognition, markers of neuropsychiatric conditions from brain activity at rest, algorithmic speedups for matrix factorization on huge datasets…
To my friends developing data science for the social media, marketing, and advertising industries,
It is time to accept that we have our share of responsibility in the outcome of the US elections and the vote on Brexit. We are not creating the society that we would like. Facebook, Twitter …
I have built static binaries of Unision 2.48 for ARM
New persistence in joblib enables low-overhead storage of big data contained in arbitrary objects
At MLOSS 15 we brainstormed on reproducible science, discussing why we care about software in computer science. Here is a summary blending notes from the discussions with my opinion.
“Without engineering, science is not more than philosophy” — the community
How do we enable better Science? Why do we do software …
After 6 months of efforts, We just released version 0.2 of nilearn, dedicated to making machine learning in neuroimaging easier and more powerful.
This release integrates the features of the july sprint, and more.
Better documentation …
My research group is looking to fill a post-doc position on learning biomarkers from functional connectivity.
The challenge is to use resting-state fMRI at the level of a population to understand how intrinsic functional connectivity captures pathologies and other cognitive phenotypes. Rest fMRI is a promising tool for …
Note
The 2015 edition of the machine learning open source software (MLOSS) workshop was full of very mature discussions that I strive to report here.
I give links to the videos. Some machine-learning researchers have great thoughts about growing communities of coders, about code as a process and a deliverable …
A couple of weeks ago, we had in Paris the second international nilearn sprint, dedicated to making machine learning in neuroimaging easier and more powerful.
It was such a fantastic experience, as nilearn is really shaping up as a simple yet powerful tool, and there is a lot of enthusiasm …
Note
tl;dr: Reproducibilty is a noble cause and scientific software a promising vessel. But excess of reproducibility can be at odds with the housekeeping required for good software engineering. Code that “just works” should not be taken for granted.
This post advocates for a progressive consolidation effort of scientific …
