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BigNeuro 2017: Analyzing brain data from nano to macroscale
Eva Dyer · Gregory Kiar · William Gray Roncal · · Konrad P Koerding · Joshua T Vogelstein

Sat Dec 09 08:00 AM -- 06:30 PM (PST) @ 204
Event URL: http://evadyer.github.io/NIPS-bigneuro-2017.html »

Datasets in neuroscience are increasing in size at alarming rates relative to our ability to analyze them. This workshop aims at discussing new frameworks for processing and making sense of large neural datasets.

The morning session will focus on approaches for processing large neuroscience datasets. Examples include: distributed + high-performance computing, GPU and other hardware accelerations, spatial databases and other compression schemes used for large neuroimaging datasets, online machine learning approaches for handling large data sizes, randomization and stochastic optimization.

The afternoon session will focus on abstractions for modelling large neuroscience datasets. Examples include graphs, graphical models, manifolds, mixture models, latent variable models, spatial models, and factor learning.

In addition to talks and discussions, we plan to have papers submitted and peer reviewed. Workshop “proceedings” will consist of links to unpublished arXiv or bioarXiv papers that are of exceptional quality and are well aligned with the workshop scope. Some accepted papers will also be invited for an oral presentation; the remaining authors will be invited to present a poster.

Author Information

Eva Dyer (Georgia Institute of Technology)

Eva Dyer is an Assistant Professor in the Wallace H. Coulter Department of Biomedical Engineering at the Georgia Institute of Technology and Emory University. Dr. Dyer completed her M.S. and Ph.D degrees in Electrical & Computer Engineering at Rice University in 2011 and 2014, respectively. Eva's research interests lie at the intersection of data science, machine learning, and neuroscience.

Gregory Kiar (McGill University)

Greg Kiar is a PhD Student in the Department of Biomedical Engineering in the Montreal Neurological Institute with Alan C. Evans at McGill University. Mr. Kiar completed his B.En in Biomedical and Electrical Engineering from Carleton University in 2014, and his M.S.E in Biomedical Engineering from Johns Hopkins University with Joshua T. Vogelstein in 2016, in which time he developed and optimized a pipeline for structural connectome estimation at the macroscale in humans. Greg is a Healthy Brain for Healthy Lives PhD fellow, and his work continues to primarily focus on increasing the accessibility, robustness, and reliability of computational neuroscience pipelines and workflows.

William Gray Roncal (Johns Hopkins University)
Konrad P Koerding (Northwestern)
Joshua T Vogelstein (The Johns Hopkins University)

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