Broad decoding models that can specialize to discriminate closely-related mental process with limited data
TL;DR
Decoding models can help isolating which mental processes are implied by the activation of given brain structures. But to support a broad conclusion, they must be trained on many studies, a difficult problem given …
My current research spans wide: from brain sciences to core data science. My overall interest is to build methodology drawing insights from data for questions that have often been addressed qualitatively. If I can highlight a few publications from 2019 [1], the common thread would be computational statistics, from dirty …
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 …
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…
Eickenberg et …
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 …
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 …
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
Work with us to leverage leading-edge machine learning for neuroimaging
At Parietal, my research team, we work on improving the way brain images are analyzed, for medical diagnostic purposes, or to understand the brain better. We develop new machine-learning tools and investigate new methodologies for for quantifying brain function from …
Work with us on putting machine learning in the hand of cognitive scientists
Parietal is a research team that creates advanced data analysis to mine functional brain images and solve medical and cognitive science problems. Our day to day work is to write machine-learning and statistics code to understand and …
At the request of a friend, I am putting up some of the posters that I recently presented at conferences.
Large-scale functional-connectivity graphical models for individual subjects using population prior.
This is a poster for our NIPS work
Multi-subject dictionary learning to segment an atlas of brain spontaneous activity …
I am currently changing jobs and changing countries. This is why I have been really bad at dealing with questions on the mailing-lists, bug-reports or feature requests.
So far I have been working as a physicist, doing atomic physics (Bose Einstein Condensation). I studied quantum physics, mostly theory, and …
