[Comp-neuro] INNS BigData 2015 San Francisco - New Conference! Calls for Papers, Special Sessions, Tutorials and Workshops!

Asim Roy ASIM.ROY at asu.edu
Wed Jan 14 03:09:45 CET 2015


Apologies for cross-posting. Note the plenary talk by Juergen Schmidhuber (Prof. Jürgen Schmidhuber<http://people.idsia.ch/~juergen/>) on Deep Learning. There will also be a tutorial and a workshop on Deep Learning by Juergen Schmidhuber and Dong Yu of Microsoft Research (Dong Yu<http://research.microsoft.com/en-us/people/dongyu/>, Microsoft Research<http://research.microsoft.com/en-us/>).
Note the deadlines for submission of proposals for special sessions, tutorials and workshops. See you in San Francisco.
<http://www.innsbigdata.org/>
[http://innsbigdata.org/wp-content/uploads/2014/09/banner.jpg]<http://www.innsbigdata.org/>

INNS Conference on Big Data 2015

New approaches to solving hard Big Data problems!
8 - 10 August 2015, San Francisco www.innsbigdata.org<http://www.innsbigdata.org/>
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.

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 Twitter<https://twitter.com/inns_bigdata>, Facebook<https://www.facebook.com/innsbigdata15/> and Google+<https://plus.google.com/112891798437473029046> pages!
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[http://innsbigdata.org/wp-content/uploads/2014/09/new.gif]PLENARY TALK<http://innsbigdata.org/>

[http://innsbigdata.org/wp-content/uploads/2014/11/juergen.jpg]
DEEP LEARNING
Prof. Jürgen Schmidhuber<http://people.idsia.ch/~juergen/>, Professor of Artificial Intelligence at the University of Lugano<http://www.inf.usi.ch/index.htm>, and the Swiss AI Lab IDSIA.<http://www.idsia.ch/>
Since age 15 or so, Prof. Jürgen Schmidhuber’s main scientific ambition has been to build an optimal scientist through self-improving Artificial Intelligence (AI), then retire. He has pioneered self-improving general problem solvers since 1987, and Deep Learning Neural Networks (NNs) since 1991. The Long Short-Term Memory (LSTM) recurrent NNs (RNNs), developed by his research groups at the Swiss AI Lab IDSIA & USI & SUPSI & TU Munich, were the first RNNs to win official international contests. LSTM recently helped to improve connected handwriting recognition, speech recognition, machine translation, optical character recognition, image caption generation, and are now in use at Google, Microsoft, IBM, and many other companies. IDSIA’s Deep Learners were also the first to win object detection and image segmentation contests, and achieved the world’s first superhuman visual classification results, winning nine international competitions in machine learning & pattern recognition (more than any other team). Since 2009 he has been member of the European Academy of Sciences and Arts. He has published over 300 peer-reviewed papers, earned seven best paper/best video awards, and is recipient of the 2013 Helmholtz Award of the International Neural Networks Society.
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Important Dates:<http://innsbigdata.org/important-dates/>

  *   Paper submission:<http://innsbigdata.org/paper-submission/> March 22, 2015.
  *   Paper Decision Notification: May 22, 2015.
  *   Camera Ready Submission of papers: June 8, 2015.


Call for Special Sessions:<http://innsbigdata.org/special-sessions/>

  *   Deadline: January 22, 2015
  *   Any proposal can be sent by e-mail to:
INNSBigData2015SpecialSessions at gmail.com<mailto:INNSBigData2015SpecialSessions at gmail.com>


Call for Tutorials<http://innsbigdata.org/tutorials/> and Workshops:<http://innsbigdata.org/workshops/>

  *   Deadline: January 22, 2015
  *   Any questions can be sent to the Tutorials & Workshops Chairs:
Marley Vellasco (PUC-Rio. Rio de Janeiro. Brazil)<mailto:Marley%20Vellasco%20(PUC-Rio.%20Rio%20de%20Janeiro.%20Brazil)%20%3cmarley at ele.puc-rio.br%3e>
and Trevor Martin (Univ. of Bristol, UK)<mailto:Trevor%09Martin%20(Univ.%20of%20Bristol.%20UK)%20%3ctrevor.martin at bristol.ac.uk%3e>.


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[http://innsbigdata.org/wp-content/uploads/2014/09/new.gif]The Elsevier USD 2000 Big Data Best Paper Award:<http://innsbigdata.org/best-paper-award/>

This award recognizes the best paper presented at the INNS Big Data conference. Both application and theoretical papers will be considered.
It will be awarded by the Big Data Analytics Section of the International Neural Network Society and is sponsored by Elsevier.
The Award consists of a plaque and a $2000 honorarium.

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Dr. Fen Zhao Talk<http://innsbigdata.org>

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,
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.

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PLENARY SPEAKERS:<http://innsbigdata.org/>

[http://innsbigdata.org/wp-content/uploads/2014/11/Bin-Yu.jpg]
Prof. Bin Yu<https://www.stat.berkeley.edu/~binyu/Site/Welcome.html>, Chancellor´s Professor, University of California<http://www.universityofcalifornia.edu/>, Berkeley.
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. She is a Member of the U.S. National Academy of Sciences, and a Fellow of the American Academy of Arts and Sciences.


[http://innsbigdata.org/wp-content/uploads/2014/11/Raghu.jpg]
Prof. Raghu Ramakrishnan<http://pages.cs.wisc.edu/~raghu/>, Head of Cloud and Information Services Lab (CISL) and big data team, Microsoft<http://research.microsoft.com/en-us/events/fs2013/raghu-ramakrishnan_bigdataplatforms.pdf/>
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.

[http://innsbigdata.org/wp-content/uploads/2014/11/brenda.jpg]
Prof. Brenda Dietrich,<https://www-03.ibm.com/ibm/history/witexhibit/wit_fellows_dietrich.html> IBM Fellow and VP, Leads the Emerging Technologies Team for IBM Watson, IBM<http://www.ibm.com/ibm/ideasfromibm/us/ibm_fellows/>
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.
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[http://innsbigdata.org/wp-content/uploads/2014/09/new.gif]TUTORIALS & WORKSHOPS:

TUTORIALS

  *   Deep Learning - Profs. Juergen Schmidhuber<http://people.idsia.ch/~juergen/> (University of Lugano<http://www.inf.usi.ch/index.htm>, and the Swiss AI Lab IDSIA<http://www.idsia.ch/>) and Dong Yu<http://research.microsoft.com/en-us/people/dongyu/> (Microsoft Research<http://research.microsoft.com/en-us/>)
  *   Introduction to How Brain Deals with Big Data - Juyang Weng<http://www.cse.msu.edu/~weng/> (Michigan State University<http://www.msu.edu/>)
  *   Platforms and Algorithms for Big Data Analytics - Prof. Chandan K. Reddy<http://www.cs.wayne.edu/~reddy/> (Wayne State University<http://wayne.edu/>)
  *   Big Data Analytics, Machine Learning Cognitive Algorithms and the Mind - Prof. Leonid I. Perlovsky<http://www.northeastern.edu/cos/psychology/people/faculty/> (Northeastern University<http://www.northeastern.edu/>)
  *   Spiking Neural Networks and Neuromorphic Spatio-Temporal Data Machines - Prof. Nikola Kasabov <http://www.aut.ac.nz/profiles/nikola-kasabov> (Auckland University of Technology<http://www.aut.ac.nz/>)
  *   Online Learning for Big Data Analytics - Prof. Irwin King<https://www.cse.cuhk.edu.hk/irwin.king.new/> (Chinese University of Hong Kong<http://www.cuhk.edu.hk/english/index.html>)


WORKSHOPS

  *   Deep Learning - Profs. Juergen Schmidhuber<http://people.idsia.ch/~juergen/> and Dong Yu<http://research.microsoft.com/en-us/people/dongyu/>
  *   Neuromorphic Spatio-Temporal Big Data Machines - Prof. Nikola Kasabov <http://www.aut.ac.nz/profiles/nikola-kasabov>
  *   Neural networks and wearable devices - Prof. Danilo Mandic<http://www.commsp.ee.ic.ac.uk/~mandic/>
  *   Big Data and Power Systems - Profs. Dejan Sobajic and Kumar Venayagamoorthy
  *   Crowd Behaviour and Big Data - Profs. Chrisina Jayne and Mehmed Kantardzic


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Neural Networks Special Issue: Neural Network Learning in Big Data<http://www.journals.elsevier.com/neural-networks/call-for-papers/special-issue-on-neural-network-learning-in-big-data/>
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).
Manuscript submission due: January 15, 2015

Big Data Analytics Section @ INNS<http://www.inns.org/big-data-section>
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.
Anyone who is interested to know more is encouraged to visit the homepage of the INNS-BDA Section<http://www.inns.org/big-data-section>.
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We have an enthusiastic team working hard on the conference program and events. Start thinking about your paper submissions.
Our Chairs for the [Special Sessions, Tutorials, and Workshops] are expecting your proposals soon - email them to discuss your ideas.
Come to San Francisco next summer to take part in the future of BigData, and to have fun!!

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GENERAL CHAIRS:<http://innsbigdata.org/committees/>

Asim Roy<https://webapp4.asu.edu/directory/person/9973> (email<mailto:Asim%20Roy.%20General%20Co-Chair.%20INNS%20BigData2015.%20Arizona%20StateU.%20USA%20%3cASIM.ROY at asu.edu%3e>)
INNS BigData General Co-Chair
Arizona State University, USA
INNS Board of Governors


Plamen Angelov<http://www.lancaster.ac.uk/staff/angelov/> (email<mailto:Plamen%20Angelov.%20General%20Co-Chair.%20INNS%20BigData2015.%20Lancaster%20U.%20UK%20%3cp.angelov at lancaster.ac.uk%3e>)
INNS BigData General Co-Chair
Lancaster University, UK
Chair in Intelligent Systems


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Many thanks to our Sponsors:

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