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Supervised Neural Time Series 2017

March 27th - 31st, New York

Learn more


Holy sh*t! We're in a paradigm shift.

In less than a decade, machine learning and scalable computing have demonstrated how knowledge can be extracted from complex datasets. The primary workhorse of this success has been free open source software (FOSS). With its strong emphasis on API design, the FOSS culture has made it less effortful to plan, develop in teams, re-use, distribute, teach, optimize & scale data analysis efforts. Coding sprints are a way to focus development efforts and share best practices that generalize across a range of application domains.


Supervised Neural Time Series is a week-long coding sprint to gather established data scientists, who specialize in high-dimensional neural time series. Together, we will work on advancing popular or upcoming FOSS projects that enable the analysis of a broad class of neural recordings: extracellular neurophysiology (spike trains), electro-corticography (ECoG) and magneto-/electro-encephalography (MEG/EEG).

  • MNE is a single suite of tools for pre-processing, source-modeling, decoding and visualization of MEG, EEG and ECoG data.
  • Pyglmnet enables generalized linear modeling with advanced regularization.
  • Spykes offers 101 analysis tools for spike trains and visualization, including rasters, PSTHs, tuning curves, RF estimation and population decoding.
  • PyRiemann offers tools to characterize the covariance structure of high-dimensional signals with Riemannian geometry.

We are looking for established data science developers to both contribute and bring their own projects. Although the organizers specialize in neural time series, we are looking to productively engage with data scientists from fields with similarly structure data (finance, musicology, speech processing, climate science, seismology, kinect and radar analyses, etc.). In keeping with this push for diversity, we particularly welcome women and underrepresented minorities.

Get Ready!

The detailed Pull Requests are described on the github repository of each package (MNE, pyGLMnet, spykes, pyRiemann). During the sprint, we'll chat on gitter.

If you are not a core developer, you will need to:

  • contact me (jeanremi.king [at] to sign-up to the coding sprint and be allowed in the building.
  • ensure that you have installed the master (dev) branch of each package.
  • you have the tools to contribute to FOSS (See MNE recommendation).
  • identify a specific pull request.
  • contact the assigned core developer(s) in advance to discuss the requirements.


  • open issues that need to be addressed in the coding sprint.
  • ensure they contain explicit descriptions, a difficulty level and an assignee.


  • Multi-winner of Kaggle competitions and has specialized in decoding MEG and EEG signals using Riemannian Geometry.
  • Assistant Professor at Telecom ParisTech Université Paris-Saclay, Scikit-Learn core developer and working on statistical machine learning and neuroscience data processing.
  • Graduate student at University of California Berkeley and has specialized in continuous encoding models.
  • Researcher at INRIA and has specialized in automated and large-scale analyses of EEG and MEG data.
  • Research scientist at University of Washington, SciPy maintainer, and working on M/EEG and pupillometry data processing.
  • Postdoc at Uni. of Frankfurt and has specialized in continuous encoding models.
  • Postdoctoral Fellow at NYU and has specialized in supervised machine learning applied to neural time series.
  • Graduate student at Telecom ParisTech Université Paris-Saclay and specializes in denoising and modeling M/EEG signals.
  • Research Associate at the Rehabilitation Institute of Chicago, and has specialized in encoding and decoding modeling of M/EEG and spikes recordings.
  • Mozilla Science Fellow and a postdoc at Stanford University and has specialized in decoding M/EEG signals and eye movement behavior.

The current developers focus on neural time series. However, we are particularly interested in productive exchange with other scientific fields that encounter similarly structured signals (finance, musicology, speech processing, climate science, seismology, kinect and radar analyses, etc).


The sprint will take place Monday March 27th - Friday March 31st, 2017. The day-by-day schedule is TBD.


jeanremi.king [at]