INNS Big Data 2015: Updated Call for Papers
Big Data 2015 - INNS - San Francisco
[Apologies for cross-postings]
IMPORTANT
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Papers Submission Deadline - March 22nd, 2015
Please submit your papers, and note that as our schedule is already compressed, there is little room for extensions after that date.
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The inaugural INNS Big Data 2015 conference is the place to be if you seek to advance your career in big data research. This is your chance to mingle with and learn from the big names in the field of big data and deep learning. Your attendance at INNS Big Data 2015 will open up many opportunities for you.
So, do not hesitate anymore. Read on to find out how you can submit your best works to the inaugural INNS Big Data 2015 conference and get noticed! See you at the conference venue this August!
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The inaugural INNS Big Data 2015 conference
August 8-10, 2015, San Francisco, USA
CALL FOR PAPERS
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Homepage: http://www.innsbigdata.org
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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, Facebook and Google+ pages!
Important Dates
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* Paper Submission March 22, 2015
* Paper Decision Notification May 22, 2015
* Camera Ready Submission of papers June 8, 2015
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Elsevier Best Paper Award
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* 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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Plenary Speakers
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* Brian McCarson, Senior Principal Engineer, Intel IoT Platform
* Fen Zhao, Staff Associate at the office of the Assistant Director for Computer & Information Science & Engineering, National Science Foundation
* Bin Yu, Chancellor’s Professor, University of California , Berkeley
* Raghu Ramakrishnan, Head of Cloud and Information Services Lab (CISL) and big data team, Microsoft
* Brenda Dietrich, IBM Fellow and VP, Leads the Emerging Technologies Team for IBM Watson, IBM
* Juergen Schmidhuber, Scientific Director, IDSIA
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Tutorials
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* Deep Learning - Profs. Juergen Schmidhuber (University of Lugano, and the Swiss AI Lab IDSIA) and Dong Yu (Microsoft Research)
* Platforms and Algorithms for Big Data Analytics - Prof. Chandan K. Reddy (Wayne State University)
* Online Learning for Big Data Analytics - Prof. Irwin King (Chinese University of Hong Kong)
* Introduction to How Brain Deals with Big Data - Juyang Weng (Michigan State University)
* Big Data Analytics, Machine Learning Cognitive Algorithms and the Mind - Prof. Leonid I. Perlovsky (Northeastern University)
* A Perspective on Emerging Neuromemristive Hardware Architecture - Profs. Manan Suri (IIT - Delhi) and Dhireesha Kudithipudi (RIT)
* Spiking Neural Networks and Neuromorphic Spatio-Temporal Data Machines - Prof. Nikola Kasabov (Auckland University of Technology)
* Neural Networks and Wearable Devices - Prof. Danilo P. Mandi (Imperial College)
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Workshops
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* Deep Learning - Profs. Juergen Schmidhuber and Dong Yu
* Neuromorphic Spatio-Temporal Big Data Machines - Prof. Nikola Kasabov
* Neural networks and wearable devices - Prof. Danilo Mandic
* Big Data and Power Systems - Profs. Dejan Sobajic and Kumar Venayagamoorthy
* Crowd Behaviour and Big Data - Profs. Chrisina Jayne and Mehmed Kantardzic
* Automatic Machine Learning - Prof. Isabelle Guyon
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Paper Submission and Publication
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* Prospective authors should submit a full-length draft manuscript (8 pages), including figures, tables and references using the Elsevier Standard template without page numbers.
* The full papers should be submitted to the INNS-BigData’2015 EasyChair online submission website: https://easychair.org/conferences/?conf=innsbigdata2015
* Conference proceedings will be published by Elsevier as a separate issue of Procedia Computer Science, which is an open access publication and is part of Science Direct electronic service.
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SPONSORS
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* International Neural Network Society (INNS)
* Elsevier
* United Technologies Research Center
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General Chairs
* Plamen Angelov, Lancaster University, UK
* Asim Roy, Arizona State University, Tempe, USA
Big Data Analytics Section @ INNS
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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. By actively promoting the use of neural networks for big data analytics, the INNS-BDA section is dedicated towards bringing the neural network field to greater heights. Anyone who is interested to know more is encouraged to visit the homepage of the INNS-BDA Section at http://www.inns.org/big-data-section.
Topics and Areas include, but not limited to:
* Autonomous, online, incremental learning – theory, algorithms and applications in big data
* High dimensional data, feature selection, feature transformation – theory, algorithms and applications for big data
* Scalable algorithms for big data
* Learning algorithms for high-velocity streaming data
* Kernel methods and statistical learning theory
* Big data streams analytics
* Deep neural network learning
* Machine vision and big data
* Brain-machine interfaces and big data
* Cognitive modeling and big data
* Embodied robotics and big data
* Fuzzy systems and big data
* Evolutionary systems and big data
* Evolving systems for big data analytics
* Neuromorphic hardware for scalable machine learning
* Parallel and distributed computing for big data analytics (cloud, map-reduce, etc.)
* Big data and collective intelligence/collaborative learning
* Big data and hybrid systems
* Big data and self-aware systems
* Big Data and infrastructure
* Big data analytics and healthcare/medical applications
* Big data analytics and energy systems/smart grids
* Big data analytics and transportation systems
* Big data analytics in large sensor networks
* Big data and machine learning in computational biology, bioinformatics
* Recommendation systems/collaborative filtering for big data
* Big data visualization
* Online multimedia/ stream/ text analytics
* Link and graph mining
* Big data and cloud computing, large scale stream processing on the cloud