From a scientific perspective, 2018 [1] was once again extremely exciting thank to awesome collaborators (at Inria, with DirtyData, and our local scikit-learn team). Rather than going over everything that we did in 2018, I would like to give a few highlights: We published major work using machine learning to …
In my opinion the scientific highlights of 2017 for my team were on multivariate predictive analysis for brain imaging: a brain decoder more efficient and faster than alternatives, improvement clinical predictions by predicting jointly multiple traits of subjects, decoding based on the raw time-series of brain activity, and a personnal …
Two week ago, we held in Paris a large international sprint on scikit-learn. It was incredibly productive and fun, as always. We are still busy merging in the work, but I think that know is a good time to try to summarize the sprint.
We had a …
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 …
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 …
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 …
Note
This year again we will have an exciting workshop on the leading-edge machine-learning open-source software. This subject is central to many, because software is how we propagate, reuse, and apply progress in machine learning.
Want to present a project? The deadline for the call for papers is Apr 28th …
We, Parietal team at INRIA, are recruiting software developers to work on open source machine learning and neuroimaging software in Python.
In general, we are looking for people who:
- have a mathematical mindset,
- are curious about data (ie like looking at data and understanding it)
- have an affinity for problem-solving …
The steering committee of PRNI (Pattern Recognition for NeuroImaging) is opening a call for bid to organize the conference in June 2016, in Europe
