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ParLearning 2012 CALL FOR PAPERS
UPDATED on 12/19/2011
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ParLearning 2012
Workshop on Parallel and Distributed Computing
for Machine Learning and Inference Problems
May 25, 2012
Shanghai, China
In Conjunction with IPDPS 2012

https://researcher.ibm.com/researcher/view_project.php?id=2591
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HIGHLIGHTS
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* Foster collaboration between HPC community and AI community
* Applying HPC techniques for learning problems
* Identifying HPC challenges from learning and inference
* Explore a critical emerging area with strong academia and industry interest
* Great opportunity for researchers worldwide for collaborating with Chinese Academia and Industry


CALL FOR PAPERS
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This workshop is one of the major meetings for bringing together researchers in High Performance Computing and Artificial Intelligence to discuss state-of-the-art algorithms, identify critical applications that benefit from parallelization, prospect research areas that require most convergence and assess the impact on broader technical landscape. This is also a great opportunity for researchers worldwide for collaborating with Chinese Academia and Industry.

Authors are invited to submit manuscripts of original unpublished research that demonstrate a strong interplay between parallel/distributed computing techniques and learning/inference applications, such as algorithm design and libraries/framework development on multicore/ manycore architectures, GPUs, clusters, supercomputers, cloud computing platforms that target applications including but not limited to:

Learning and inference using large scale Bayesian Networks
Large scale inference algorithms using parallel TPIC models, clustering and SVM etc.
Parallel natural language processing (NLP).
Semantic inference for disambiguation of content on web or social media
Discovering and searching for patterns in audio or video content
On-line analytics for streaming text and multimedia content
Comparison of various HPC infrastructures for learning
Large scale learning applications in search engine and social networks
Distributed machine learning tools (e.g., Mahout and IBM parallel tool)
Real-time solutions for learning algorithms on parallel platforms


IMPORTANT DATE
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Workshop Paper Due January 18, 2012
Author Notification February 1, 2012
Camera-ready Paper Due February 21, 2012


PAPER GUIDELINES
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Submitted manuscripts may not exceed 10 single-spaced double-column pages using 10-point size font on 8.5x11 inch pages (IEEE conference style), including figures, tables, and references. More format requirements will be posted on the IPDPS web page (www.ipdps.org) shortly after the author notification Authors can purchase up to 2 additional pages for camera-ready papers after acceptance. Please find details on www.ipdps.org. All papers must be submitted through the EDAS portal. Students with accepted papers have a chance to apply for a travel award. Please find details at www.ipdps.org.

Submit your paper using EDAS portal for ParLearning: http://edas.info/N11575


PROCEEDINGS
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All papers accepted by the workshop will be included in the proceedings of the IEEE International Symposium on Parallel & Distributed Processing, Workshops and PhD Forum (IPDPSW), indexed in EI and possibly in SCI.


ORGANIZATION
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General Co-chairs:
Sutanay Choudhury, Pacific Northwest National Laboratory, USA
George Chin, Pacific Northwest National Laboratory, USA
Yinglong Xia, IBM T.J. Watson Research Center, USA

Local Chair:
Yihua Huang, Nanjing University, China

Program Co-chairs:
John Feo, Pacific Northwest National Laboratory, USA
Chandrika Kamath, Lawrence Livermore National Laboratory, USA
Anshul Gupta, IBM T.J. Watson Research Center, USA

Program Committee:
Arindam Banerjee, University of Minnesota, USA
Enhong Chen, Univ. of Sci. & Tech. of China, China
Weizhu Chen, Microsoft Research, China
Jatin Chhugani, Intel Corp., USA
Edmond Chow, Georgia Tech, USA
Tina Eliassi-Rad, Rutgers University, USA
Mahantesh Halappanavar, Pacific Northwest National Lab, USA
Lawrence B. Holder, Washington State U., USA
Yihua Huang, Nanjing University, China
Yan Liu, University of Southern California, USA
Arindam Pal, Indian Institute of Technology, India
Yangqiu Song, Microsoft Research, China
Oreste Villa, Pacific Northwest National Lab, USA
Jun Wang, IBM T.J. Watson Research Center, USA
Yi Wang, Tencent Holdings Lt., China
Haixun Wang, Microsoft Research, China
Lexing Xie, Australian National University, Australia


KEYNOTE SPEAKER
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Haixun Wang
Microsoft Research, China


CONTACT
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Should you have any questions regarding the workshop or this webpage, please contact yxia ~AT~ us DOT ibm DOT com, or sutanay DOT choudhury ~AT~ pnnl DOT gov.