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<title>INNS Conference on Big Data 2015 - International Neural Network Society</title>
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<p class="MsoNormal"><span style="color:#1F497D">Apologies for cross-posting.</span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<div>
<p class="MsoNormal" align="center" style="text-align:center"><span style="color:blue"><a href="http://www.innsbigdata.org/"><span style="text-decoration:none"><img border="0" width="899" height="208" id="_x0000_i1025" src="http://innsbigdata.org/wp-content/uploads/2014/09/banner.jpg" name="IJCNN Header"></span></a><span class="MsoHyperlink"><o:p></o:p></span></span></p>
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<o:p></o:p></p>
<h1 align="center" style="margin:0in;margin-bottom:.0001pt;text-align:center">INNS Conference on Big Data 2015<o:p></o:p></h1>
<p align="center" style="text-align:center"><b><span style="font-size:13.5pt">New approaches to solving hard Big Data problems!<br>
8 - 10 August 2015, San Francisco <a href="http://www.innsbigdata.org/">www.innsbigdata.org</a></span>
</b><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:12.0pt"><br>
<br>
The aim of the INNS BigData conference is to promote new advances and research directions in efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms), implementations on different
computing platforms (e.g. neuromorphic, GPUs, clouds, clusters) and applications of Big Data Analytics to solve real-world problems (e.g. weather prediction, transportation, energy management). Please refer to our website for a more detailed list of topics.<br>
<br>
Being INNS' inaugural conference on the theme of big data, we are especially motivated to synthesize ideas, promote activities and generate broad interest in areas where neural networks have many unique advantages. We also have
<a href="https://twitter.com/inns_bigdata">Twitter</a>, <a href="https://www.facebook.com/innsbigdata15/">
Facebook</a> and <a href="https://plus.google.com/112891798437473029046">Google+</a> pages!<o:p></o:p></p>
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<p align="center" style="margin-bottom:12.0pt;text-align:center"><a href="http://innsbigdata.org/special-sessions/">Call for Special Sessions</a>
<br>
Any proposal can be sent by e-mail to:<a href="mailto:INNSBigData2015SpecialSessions@gmail.com"><br>
INNSBigData2015SpecialSessions@gmail.com</a><br>
<b><u><span style="color:#FF0066">Deadline</span></u><span style="color:#FF0066">: January 22, 2015</span></b><o:p></o:p></p>
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<td valign="top" style="padding:0in 0in 0in 0in">
<p align="center" style="margin-bottom:12.0pt;text-align:center"><a href="http://innsbigdata.org/tutorials/">Call for Tutorials</a>
<br>
Any questions can be sent to the Tutorials Chairs: <br>
<a href="mailto:Marley%20Vellasco%20(PUC-Rio.%20Rio%20de%20Janeiro.%20Brazil)%20%3cmarley@ele.puc-rio.br%3e">Marley Vellasco (PUC-Rio. Rio de Janeiro. Brazil)</a>
<br>
and <a href="mailto:Trevor%09Martin%20(Univ.%20of%20Bristol.%20UK)%20%3ctrevor.martin@bristol.ac.uk%3e">
Trevor Martin (Univ. of Bristol, UK)</a>.<br>
<b><u><span style="color:#FF0066">Deadline</span></u><span style="color:#FF0066">: January 22, 2015</span></b><o:p></o:p></p>
</td>
<td valign="top" style="padding:0in 0in 0in 0in">
<p align="center" style="margin-bottom:12.0pt;text-align:center"><a href="http://innsbigdata.org/workshops/">Call for Workshops</a>
<br>
For further details contact the Workshop Chairs: <br>
<a href="mailto:Marley%20Vellasco%20(PUC-Rio.%20Rio%20de%20Janeiro.%20Brazil)%20%3cmarley@ele.puc-rio.br%3e">Marley Vellasco (PUC-Rio. Rio de Janeiro. Brazil)</a>
<br>
and <a href="mailto:Trevor%09%09%20%20Martin%20(Univ.%20of%20Bristol.%20UK)%20%3ctrevor.martin@bristol.ac.uk%3e">
Trevor Martin (Univ. of Bristol, UK)</a>.<br>
<b><u><span style="color:#FF0066">Deadline</span></u><span style="color:#FF0066">: January 22, 2015</span></b><o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
<p align="center" style="text-align:center"><img border="0" width="60" height="35" id="_x0000_i1026" src="http://innsbigdata.org/wp-content/uploads/2014/09/new.gif" name="Bin Yu"><b><span style="font-size:13.5pt"><a href="http://innsbigdata.org/best-paper-award/">The
Elsevier USD 2000 Big Data Best Paper Award:</a></span></b> <br>
<br>
This award recognizes the best paper presented at the INNS Big Data conference. Both application and theoretical papers will be considered.
<br>
It will be awarded by the Big Data Analytics Section of the International Neural Network Society and is sponsored by Elsevier.
<br>
The Award consists of a plaque and a <b>$2000</b> honorarium. <o:p></o:p></p>
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<p align="center" style="text-align:center"><img border="0" width="60" height="35" id="_x0000_i1028" src="http://innsbigdata.org/wp-content/uploads/2014/09/new.gif" name="Fen Zhao"><b><span style="font-size:13.5pt"><a href="http://innsbigdata.org">Dr. Fen Zhao
Talk</a></span></b> <br>
<br>
Dr. Fen Zhao, a Staff Associate at the Office of the Assistant Director (OAD) for Computer & Information Science & Engineering (CISE) at the National Science Foundation,
<br>
will give a talk on national big data R&D initiative and on building public-private partnerships around CISE's Big Data, next generation internet, and cybersecurity R&D portfolios.
<o:p></o:p></p>
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<p align="center" style="text-align:center"><b><span style="font-size:13.5pt"><a href="http://innsbigdata.org/">Plenary Speakers:</a></span></b>
<br>
<br>
<img border="0" width="100" height="130" id="_x0000_i1030" src="http://innsbigdata.org/wp-content/uploads/2014/11/Bin-Yu.jpg" name="Bin Yu"><br>
<a href="https://www.stat.berkeley.edu/~binyu/Site/Welcome.html">Prof. Bin Yu</a>, Chancellor´s Professor,
<a href="http://www.universityofcalifornia.edu/">University of California</a>, Berkeley.
<o:p></o:p></p>
<p class="MsoNormal">Bin Yu is Chancellor’s Professor in the Departments of Statistics and of Electrical Engineering & Computer Science at the University of California at Berkeley. She held faculty positions at UW-Madison and Yale University and was a Member
of Technical Staff at Lucent Bell Labs. She was Chair of Department of Statistics at Berkeley from 2009 to 2012, and is a founding co-director of the Microsoft Joint Lab on Statistics and Information Technology at Peking University where she is also Chair
of the scientific advisory committee of the Center for Statistical Sciences. She has published over 80 scientific papers in premier journals in statistics, machine learning, information theory, signal processing, remote sensing, neuroscience, network analysis,
and bioinformatics. <o:p></o:p></p>
<p align="center" style="text-align:center"><br>
<br>
<img border="0" width="100" height="130" id="_x0000_i1031" src="http://innsbigdata.org/wp-content/uploads/2014/11/Raghu.jpg" name="Raghu Ramakrishnan"><br>
<a href="http://pages.cs.wisc.edu/~raghu/">Prof. Raghu Ramakrishnan</a>, Head of <a href="http://research.microsoft.com/en-us/events/fs2013/raghu-ramakrishnan_bigdataplatforms.pdf/">
Cloud and Information Services Lab (CISL) and big data team, Microsoft</a> <o:p></o:p></p>
<p class="MsoNormal">Raghu Ramakrishnan heads the Cloud and Information Services Lab (CISL) in the Data Platforms Group at Microsoft, and leads development for the Big Data team. From 1987 to 2006, he was a professor at University of Wisconsin-Madison, where
he wrote the widely-used text “Database Management Systems” and led a wide range of research projects in database systems (e.g., the CORAL deductive database, the DEVise data visualization tool, SQL extensions to handle sequence data) and data mining (scalable
clustering, mining over data streams). In 1999, he founded QUIQ, a company that introduced a cloud-based question-answering service. He joined Yahoo! in 2006 as a Yahoo! Fellow, and over the next six years served as Chief Scientist for the Audience (portal),
Cloud and Search divisions, driving content recommendation algorithms (CORE), cloud data stores (PNUTS), and semantic search (“Web of Things”). Ramakrishnan has received several awards, including the ACM SIGKDD Innovations Award, the SIGMOD 10-year Test-of-Time
Award, the IIT Madras Distinguished Alumnus Award, and the Packard Fellowship in Science and Engineering.
<o:p></o:p></p>
<p align="center" style="text-align:center"><br>
<img border="0" width="100" height="130" id="_x0000_i1032" src="http://innsbigdata.org/wp-content/uploads/2014/11/brenda.jpg" name="Brenda Dietrich"><br>
<a href="https://www-03.ibm.com/ibm/history/witexhibit/wit_fellows_dietrich.html">Prof. Brenda Dietrich,</a> IBM Fellow and VP, Leads the Emerging Technologies Team for
<a href="http://www.ibm.com/ibm/ideasfromibm/us/ibm_fellows/">IBM Watson, IBM</a>
<o:p></o:p></p>
<p class="MsoNormal">Brenda Dietrich is an IBM Fellow and Vice President. She joined IBM in 1984 and has worked in the area now called analytics for her entire career, applying data and computation to business decision processes throughout IBM. For over a decade
she led the Mathematical Sciences function in the IBM Research division where she was responsible for both basic research on computational mathematics and for the development of novel applications of mathematics for both IBM and its clients. She has been the
president of INFORMS, has served on the Board of Trustees of SIAM, and is a member of several university advisory boards. She holds more than a dozen patents, has co-authored numerous publications, and frequently speaks on analytics at conferences. She was
elected to the National Academy of Engineering in 2014. She holds a BS in Mathematics from UNC and an MS and Ph.D. in OR/IE from Cornell. Her personal research includes manufacturing scheduling, services resource management, transportation logistics, integer
programming, and combinatorial duality. She currently leads the emerging technologies team for IBM Watson, extending and applying IBM’s cognitive computing technology.
<o:p></o:p></p>
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<p class="MsoNormal"><b><span style="font-size:13.5pt"><a href="http://innsbigdata.org/important-dates/">Important Dates</a></span></b>
<o:p></o:p></p>
<ul type="disc">
<li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l1 level1 lfo3">
<a href="http://innsbigdata.org/paper-submission/">Paper submission:</a><b><span style="color:#669900"> March 22, 2015</span>.</b>
<o:p></o:p></li><li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l1 level1 lfo3">
Paper Decision Notification: <b>May 22, 2015.</b> <o:p></o:p></li><li class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto;mso-list:l1 level1 lfo3">
Camera Ready Submission of papers: <b>June 8, 2015.</b> <o:p></o:p></li></ul>
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<p class="MsoNormal"><br>
We have an enthusiastic team working hard on the conference program and events. Start thinking about your paper submissions.<br>
Our Chairs for the [Special Sessions, Tutorials, and Workshops] are expecting your proposals soon - email them to discuss your ideas.<br>
<b>Come to San Francisco next summer to take part in the future of BigData, and to have fun!!</b><br>
<br>
<b><span style="font-size:13.5pt"><a href="http://www.journals.elsevier.com/neural-networks/call-for-papers/special-issue-on-neural-network-learning-in-big-data/"><i>Neural Networks</i> Special Issue: Neural Network Learning in Big Data</a></span></b><br>
For this special issue of Neural Networks, we invite papers that address many of the challenges of learning from big data. In particular, we are interested in papers on efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks,
nature-inspired and brain-inspired algorithms), implementations on different computing platforms (e.g. neuromorphic, GPUs, clouds, clusters) and applications of online learning to solve real-world big data problems (e.g. health care, transportation, and electric
power and energy management). <br>
<b>Manuscript submission due: January 15, 2015</b><br>
<br>
<br>
<b><span style="font-size:13.5pt"><a href="http://www.inns.org/big-data-section">Big Data Analytics Section @ INNS</a></span></b><br>
Considering the growing interest to process and analyse big data, the International Neural Network Society (INNS) has a new Section on Big Data Analytics (BDA) to help the neural network field position itself as a leading technology contributor to big data
analytics.<br>
Anyone who is interested to know more is encouraged to visit the homepage of the <a href="http://www.inns.org/big-data-section">
INNS-BDA Section</a>. <br>
<br>
<br>
<b><span style="font-size:13.5pt"><a href="http://innsbigdata.org/committees/">General Chairs:</a></span></b>
<o:p></o:p></p>
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<p align="center" style="text-align:center"><a href="https://webapp4.asu.edu/directory/person/9973">Asim Roy</a> (<a href="mailto:Asim%20Roy.%20General%20Co-Chair.%20INNS%20BigData2015.%20Arizona%20StateU.%20USA%20%3cASIM.ROY@asu.edu%3e">email</a>)<br>
INNS BigData General Co-Chair<br>
Arizona State University, USA<br>
INNS Board of Governors<o:p></o:p></p>
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<p align="center" style="text-align:center"><a href="http://www.lancaster.ac.uk/staff/angelov/">Plamen Angelov</a> (<a href="mailto:Plamen%20Angelov.%20General%20Co-Chair.%20INNS%20BigData2015.%20Lancaster%20U.%20UK%20%3cp.angelov@lancaster.ac.uk%3e">email</a>)<br>
INNS BigData General Co-Chair<br>
Lancaster University, UK<br>
Chair in Intelligent Systems<o:p></o:p></p>
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<b><span style="font-size:13.5pt">Many thanks to our Sponsors:</span></b><br>
<br>
<a href="http://www.inns.org/"><span style="text-decoration:none"><img border="0" id="_x0000_i1036" src="http://www.inns.org/assets/site/neural.png" name="INNS"></span></a><o:p></o:p></p>
<p style="margin-bottom:12.0pt"><a href="http://www.elsevier.com/"><span style="text-decoration:none"><img border="0" width="125" height="125" id="_x0000_i1037" src="http://innsbigdata.org/wp-content/uploads/2014/10/elsevier-logo-300x300-150x150.jpg" name="ELSEVIER"></span></a><o:p></o:p></p>
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