In the last decade, machine learning has had a growing influence on neuroimaging data handling and analysis, making it an ubiquitous component of all kinds of data analysis procedures and software. While it is clear that machine learning has the potential to revolutionize both scientific discovery and clinical diagnosis applications, continued progress requires close collaboration between statisticians, machine learning practitioners and neuroscientists. Our workshop goals are to highlight best practices, disseminate the state of the art in high dimensional methods and related tools with a focus on application to neuroimaging data analysis, and to facilitate discussions to identify the key open problems and opportunities for machine learning in neuroscience.

Topics of interest include:
- exploratory and predictive modelling of high-dimensional correlated volumic data e.g. fMRI and PET with emphasis on:
- applications to cognitive modelling and diagnosis
- resting state analyses
- interpretation and visualization of results
- statistical analysis for parameter recovery
- advances in scalable techniques and computational infrastructure
- structured prediction problems in neuroscience e.g. multilabel classification for mental processes and network prediction.
- de-noising and predictive modelling for brain-computer interfaces
- source localization for EEG and MEG
- deep learning and convolutional networks for neuroscience

Invited Speakers
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Pradeep Ravikumar (University of Texas at Austin)
Morten Mørup (Technical University of Denmark)
Alexandre Gramfort (Telecom ParisTech, CNRS LTCI)

Submissions
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We invite submissions for original papers that introduce new research developments, directions, frameworks, results, etc. in these and related areas. We also invite submissions of recently published high-quality work. Though, we note that preference will be given to original contributions. Potential participants may submit full papers (up to 8 pages in length in ICML format) or short papers (extended abstracts, 2-4 pages in length) by May 1, 2015 sent electronically via the online submission site: https://cmt.research.microsoft.com/SMLNS2015/Default.aspx
Acceptance notification: May 10th, 2015

The workshop is semi-archival: materials will be published on the website with the authors permission. The authors retain the copyright and the rights to resubmit and publish at other venues.

Organizing Committee
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Bertrand Thirion (Inria-CEA)
Lars Kai Hansen (Technical University of Denmark)
Sanmi Koyejo (Stanford University)

For further information, please contact Sanmi Koyejo (sanmi@stanford.edu)