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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
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<title>BigNeuro NIPS Workshop</title>
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<h1 class="header-navbar-logo">BigNeuro NIPS Workshop</h1>
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<ul class="header-navbar-items">
<li class="header-navbar-item"><a href="#about" class="header-navbar-link">About</a></li>
<li class="header-navbar-item"><a href="#faq" class="header-navbar-link">FAQ</a></li>
<li class="header-navbar-item"><a href="#schedule" class="header-navbar-link">Schedule</a></li>
<li class="header-navbar-item"><a href="#posters" class="header-navbar-link">Posters</a></li>
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<h2 class="header-text-headline">BigNeuro Workshop @ NIPS
<!-- <span class="beating-heart fa fa-heart"></span> -->
</h2>
<h4 class="header-text-byline">Making sense of big neural data<strong></strong></h4>
<h4 class="header-text-byline">Where: Palais des Congrès de Montréal, Montreal, Canada<strong></strong></h4>
<h4 class="header-text-byline">When: <strong>Saturday Dec. 12th, 2015</strong>
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<div id="about" class="about">
<div class="container">
<div class="row">
<div class="col-lg-12 text-center">
<h1 class="descr-title info-heading">BigNeuro 2015</h1>
<p class="lead info-para">
Advances in optics, chemistry, and physics have revolutionized the development of experimental methods for measuring neural activity and structure. Some of the next generation methods for neural recording promise extremely large and detailed measurements of the brain’s architecture and function. The goal of this workshop is to provide an open forum for the discussion of a number of important questions related to how machine learning can aid in the analysis of these next generation neural datasets. What are some of the new machine learning and analysis problems that will arise as new experimental methods come online? What are the right distributed and/or parallel processing computational models to use for these different datasets? What are the computational bottlenecks/challenges in analyzing these next generation datasets?
</p>
</div>
</div>
</div>
</div>
<div id="faq" class="faq">
<h2 class="heading-faq">FAQ</h2>
<p class="faq-additional-questions"><strong>Have a question</strong> that isn't addressed below? Email us at <a href="mailto:[email protected]">[email protected]</a></p>
<div class="faq-column">
<div class="faq-box">
<h3>What is the format?</h3>
<p> In the morning, the goal will be to discuss new experimental techniques and the computational issues associated with analyzing these datasets. The morning portion of the workshop will be organized into three hour-long sessions. Each session will start with a 30 minute overview of an experimental method, presented by a leading experimentalist in this area. Afterwards, we will have a 30 minute follow up from a computational scientist that will highlight the computational challenges associated with the technique.</p>
<p> In the afternoon, the goal will be to delve deeper into the kinds of techniques that will be needed in the future to deal with the data described in the morning. To highlight two computational approaches that we believe hold promise, we will have two hour long methods talks. These talks will be followed by a scientist with big-data experience outside of neuroscience with the goal of thinking about organization, objectives, and pitfalls.</p>
<p> Lastly we will have plenty of time for free form discussion and hold a poster session. We envision that this workshop will provide a forum for computational neuroscientists and data scientists to discuss the major challenges that we will face in analyzing big neural datasets over the next decade.</p>
</div>
<div class="faq-box">
<h3>Who can participate?</h3>
<p>We welcome participants from the machine learning community, data scientists, and neuroscientists alike!</p>
</div>
<!-- <div class="faq-box">
<h3>What can I win?</h3>
<p>It's not about winning - it's about learning, having fun, and starting the renaissance of medical innovation! In addition, winners will be chosen by a judging panel based on criteria such as technical difficulty, creativity, and impact. Many sponsors will also give out cool prizes for hacks that excel in specific categories.</p>
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</div>
<div class="faq-column">
<!-- <div class="faq-box">
<h3>What is a Medical Hackathon?</h3>
<p>Drawing on the popular concept of hackathons being held around the world, we are challenging innovators to design solutions to the most pressing medical issues of our day within 36 hours. MedHackers will work (and play) in interdisciplinary teams over the weekend, utilizing the unique skillset of each team member to build the best solution to a critical medical problem. Along the way, hackers will eat together, code together, learn together, and design together before presenting their finished product (together) to a panel of judges.</p>
</div> -->
<div class="faq-box">
<h3>But I know nothing about neuroscience?!</h3>
<p>If you don't know about neuroscience, don’t worry - we will introduce the basics behind some promising big neuro datasets and their associated computational challenges! This will set the stage for the afternoon talks and discussion, where we can talk about how machine learning can aid in analyzing these datasets. </p>
</div>
<div class="faq-box">
<h3>When is BigNeuro?</h3>
<p>
The workshop is part of the NIPS workshop program, held at the Palais des Congrès de Montréal, in Montreal, Canada. The event will take place from 9:00-18:30 on Saturday Dec 12th, 2015.
<!-- Hack@NeuroData 2015 will take place from Friday, October 2nd to Sunday, October 4th. -->
</p>
</div>
<!-- <div class="faq-box">
<h3>How will I get there?</h3>
<p>Hack@NeuroData is able to offer a limited number of travel grants for participants coming from outside the Baltimore metro area. If you are in need of assistance in attending Hack@NeuroData, and live outside the Baltimore area, please send an email to <a href="mailto:travel@[email protected]">travel@[email protected]</a>. </p>
<p>For participants coming to the Homewood Campus from other Johns Hopkins Institutions, the JHMI shuttle is recommended. The JHMI Shuttle schedule is available <a href="http://ts.jhu.edu/Shuttles/Shuttle_Schedules/Homewood_JHMI_Shuttle_Schedule.pdf">here</a>.</p>
<p>For those traveling to Hack@NeuroData by train (Amtrak and MARC Penn Line) via Baltimore Penn Station, the JHMI shuttle stops at Penn Station and heads north to the Homewood Campus (its northernmost stop). Taxis are also available at Penn Station for the 10 minute drive to the Homewood Campus, and should cost approximately $7.</p>
</div> -->
<div class="faq-box">
<h3>What is the basic schedule?</h3>
<p>
The morning session will introduce three experimental techniques that are currently generating big neural datasets. The afternoon session will focus on promising computational methods for analyzing these data. </p>
<p> At the end of the day, we will have a open discussion about how machine learning can help in analyzing and making sense of big neural datasets. </p>
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</div>
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<div id="schedule" class="schedule">
<div class="container">
<div class="row">
<h2 class="section-apply-heading text-center">Schedule</h2>
<div class="col-lg-8 col-lg-offset-2">
<p class="lead info-para text-center">Morning Session | Room 511e</p>
<table class="table">
<th>Time</th>
<th></th>
<th></th>
<tr>
<td>7:30am</td>
<td>Breakfast</td>
<td></td>
</tr>
<tr>
<td>8:50-9:00</td>
<td>Opening Remarks</td>
<td>511e</td>
</tr>
<tr>
<td>9:00-9:30</td>
<td><a href="https://www.crick.ac.uk/andreas-schaefer" style="color: rgb(204,229,255)">Andreas Schaefer</a>, High-density electrical recordings (Methods overview)</td>
<td>511e</td>
</tr>
<tr>
<td>9:30-10:00</td>
<td><a href="http://klab.smpp.northwestern.edu/" style="color: rgb(204,229,255)">Konrad Kording</a>, High-density electrical recordings (Computational approaches + challenges)</td>
<td>511e</td>
</tr>
<tr>
<td>10:00-10:20</td>
<td>Morning Coffee Break</td>
<td></td>
</tr>
<tr>
<td>10:20-10:50</td>
<td><a href="http://toliaslab.org/" style="color: rgb(204,229,255)">Andreas Tolias</a>, High-resolution tools for connectomics (Methods overview)</td>
<td>511e</td>
</tr>
<tr>
<td>10:50-11:20</td>
<td><a href="http://www.cs.jhu.edu/~randal/" style="color: rgb(204,229,255)">Randal Burns</a>, High-resolution tools for connectomics (Computational approaches + challenges)</td>
<td>511e</td>
</tr>
<tr>
<td>11:20-11:50</td>
<td><a href="http://web.stanford.edu/group/dlab/" style="color: rgb(204,229,255)">Aaron Andalman</a>, Whole-brain optical methods (Methods overview)</td>
<td>511e</td>
</tr>
<tr>
<td>11:50-12:20</td>
<td><a href="http://sapirolab.pratt.duke.edu/" style="color: rgb(204,229,255)">Guillermo Sapiro</a>, Whole-brain optical methods (Computational approaches + challenges)</td>
<td>511e</td>
</tr>
</table>
<p class="lead info-para text-center">Poster Session | Room 511e</p>
<table class="table">
<th>Time</th>
<th></th>
<th></th>
<tr>
<td>12:20-12:25</td>
<td>Furong Huang, <em> Discovering neuronal cell types and their gene expression profiles using a spatial point process mixture model</em> (Poster Spotlight)</td>
<td>511e</td>
</tr>
<tr>
<td>12:25-12:30</td>
<td>Tom Goldstein, <em>Distributed model fitting with transpose reduction ADMM</em> (Poster Spotlight)</td>
<td>511e</td>
</tr>
<tr>
<td>12:30-12:35</td>
<td>William Gray Roncal, <em>Images to graphs for inference</em> (Poster Spotlight)</td>
<td>511e</td>
</tr>
<tr>
<td>13:00-14:00</td>
<td>Poster Session</td>
<td>511e</td>
</tr>
</table>
<p class="lead info-para text-center">Afternoon Session | Room 511e</p>
<table class="table">
<th>Time</th>
<th></th>
<th></th>
<tr>
<td>14:00-14:40</td>
<td><a href="http://www.sdss.jhu.edu/~szalay/" style="color: rgb(204,229,255)">Alex Szalay</a>, Lessons learned from big data projects in cosmology </td>
<td>511e</td>
</tr>
<tr>
<td>14:40-15:00</td>
<td>Discussion with Alex Szalay</td>
<td>511e</td>
</tr>
<tr>
<td>15:00-16:00</td>
<td><a href="http://researcher.watson.ibm.com/researcher/view.php?person=us-dpwoodru" style="color: rgb(204,229,255)">David Woodruff</a>, Sketching as a Tool for Linear Algebra and Recent Developments</td>
<td>511e</td>
</tr>
<tr>
<td>16:00-16:30</td>
<td>Afternoon Coffee Break</td>
<td></td>
</tr>
<tr>
<td>16:30-17:30</td>
<td><a href="http://www.ece.rice.edu/~richb" style="color: rgb(204,229,255)">Richard G. Baraniuk</a>, Low-dimensional inference with high dimensional data</td>
<td>511e</td>
</tr>
<tr>
<td>17:30-17:40</td>
<td>Closing Remarks</td>
<td>511e</td>
</tr>
<tr>
<td>17:45-18:30</td>
<td>Open Discussion</td>
<td>511e</td>
</tr>
</table>
<!-- <p class="text-center">*All events take place in the Bloomberg Center for Physics and Astronomy and are subject to change</p> -->
</div>
</div>
</div>
</div>
<div id="posters" class="posters">
<div class="container">
<div class="row">
<h2 class="section-apply-heading text-center">Posters</h2>
<div class="col-lg-8 col-lg-offset-2">
<li> Ryan Burmeister, Gavin Taylor, Tom Goldstein: <em>Cosine Fourier bases for kernel function approximation </em></p>
<li> Tom Goldstein: <em>Distributed model fitting with transpose reduction ADMM </em></li></p>
<li>William Gray Roncal: <em>Images to graphs for inference</em></li></p>
<li> Furong Huang, Animashree Anandkumar, Christian Borgs, Jennifer Chayes, Ernest Fraenkel, Michael Hawrylycz, Ed Lein, Alessandro Ingrosso, Srinivas Turaga: <em>Discovering neuronal cell types and their gene expression profiles using a spatial point process mixture model</em></li></p>
<li> Christoph Kirst: <em>Network communication surfing on dynamical reference states</em> </li></p>
<li> Azalia Mirhoseini, Eva L. Dyer, Ebrahim Songhori, Richard G. Baraniuk, Farinaz Koushanfar: <em>RankMap: A framework for distributed learning from massive dense datasets </em></li></p>
<li>Anish K. Simhal, Will Gray Roncal, Alexander D. Baden, Kunal A. Lillaney , Kwame Kutten, Michael I. Miller, Joshua T. Vogelstein, Randal Burns, Li Ye, Raju Tomer, Karl Deisseroth, Guillermo Sapiro: <em>Computational statistics for whole brain CLARITY analysis using the Open Connectome Project</em></li></p>
<li>Prateek Tandon: <em>Poisson and bayesian estimation of low signal source and noise components </em></li></p>
<li> Ekaterina Taralova, Tony Jebara, Rafael Yuste: <em>Functional graphical models of mouse visual cortex</em></li></p>
<li>Xundong Wu, Yong Wu, Ligia Toro, Enrico Stefani: <em> A new iterative convolutional neural network algorithm improves electron microscopy image segmentation</em></li></p>
</div>
</div>
</div>
<div class="page-footer">
<div class="container">
<div class="row" id="footer-text">
<div class="col-lg-10 col-lg-offset-1 text-center">
<h4><strong>BigNeuro Workshop</strong></h4>
<p>Want to get in touch? Contact <a href="mailto:[email protected]">[email protected]</a>!</p>
<p>Organized by <a href="http://klab.smpp.northwestern.edu/wiki/index.php5/Eva_Dyer">Eva Dyer</a>, <a href="http://jovo.me/" target="_blank">Joshua T. Vogelstein</a>, <a href="http://bigneuro.com/" target="_blank">Konrad P. Kording</a>, <a href="http://www.jeremyfreeman.net/" target="_blank">Jeremy Freeman</a>, <a href="http://toliaslab.org/" target="_blank">Andreas Tolias</a>.
</p>
</p>
<p>Copyright © Hack@NeuroData 2015</p>
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