[Tccc] [CFP] ParLearning 2012 - paper submission system is now open

Yinglong Xia yinglongxia
Wed Oct 19 13:23:16 EDT 2011


 ************************************************************
           ParLearning 2012 CALL FOR PAPERS
                UPDATED on 10/18/2011
 ************************************************************
=====================================================
			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
=====================================================



HIGHLIGHTS
-----------

    * 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
---------------

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Authors are invited to submit manuscripts of original unpublished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
---------------

 Workshop Paper Due 		December 19, 2011
 Author Notification 	 	February 1, 2012
 Camera-ready Paper Due 	February 21, 2012


PAPER GUIDELINES
----------------

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
-----------

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
------------

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
---------------
Haixun Wang
Microsoft Research, China


CONTACT
-------
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.



More information about the TCCC mailing list