[hpc-announce] [IPDPS-ParLearning'2014] Deadline Extension

[Yinglong Xia]

Released on December 30, 2013
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[CFP] Deadline is extended to January 10, 2014 - ParLearning'2014

Workshop on Parallel and Distributed Computing for Large Scale Machine
Learning and Big Data Analytics (ParLearning 2014)

http://edas.info/web/parlearning2014/index.html

May 23, 2014, PHOENIX (Arizona), USA

To be held in conjunction with IPDPS 2014 (http://www.ipdps.org)



OVERVIEW

This workshop is one of the major meetings for bringing together
researchers in High Performance Computing and Artificial Intelligence
(Machine Learning, Data Mining, BigData Analytics, etc.) 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.

Data-driven computing needs no introduction today. However, the growth in
volume and heterogeneity in data seems to outpace the growth in computing
power. As soon as the data hits the processing infrastructure, determining
the value of information, finding its rightful place in a knowledge
representation and determining subsequent actions are of paramount
importance. To use this data deluge to our advantage, a convergence between
the field of Parallel and Distributed Computing and the interdisciplinary
science of Artificial Intelligence seems critical.

The primary motivation of the proposed workshop is to invite leading minds
from AI and Parallel & Distributed Computing communities for identifying
research areas that require most convergence and assess their impact on the
broader technical landscape.

TOPICS

Authors are invited to submit manuscripts of original unpublished research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: January 10, 2014
    Author Notification: February 14, 2014
    Camera-ready Paper Due:  March 14, 2014

PAPER SUBMISSION

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. 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/N15817

PROGRAM COMMITTEE

    Co-Chair: Yinglong Xia, IBM T.J. Watson Research Center, USA
    Co-Chair: Yihua Huang, Nanjing Universtiy, China

    Vice co-chair: Makoto Takizawa, Hosei University, Japan
    Vice co-chair: Ching-Hsien (Robert) Hsu, Chung Hua University, Taiwan
    Vice co-chair: Jong Hyuk Park, Kyungnam University, Korea
    Vice co-chair: Sajid Hussain, Nashville, Tennessee, USA

    Haimonti Dutta, Columbia University, USA
    Jieyue He, Southeast University, China
    Sutanay Choudhury, Pacific Northwest National Laboratory, USA
    Yi Wang, Tecent Holding Lt., China
    Zhijun Fang, Jiangxi University of Finance and Economics, China
    Wenlin Han, University of Alabama, USA
    Wan Jian, Hangzhou Dianzi University, China
    Daniel W. Sun, NICTA, Australia
    Danny Bickson, GraphLab Inc., USA
    Virendra C. Bhavsar, University of New Brunswick, Canada
    Zhihui Du, Tsinghua University, China
    Ichitaro Yamazaki, University of Tennessee, Knoxville, USA
    Gwo Giun (Chris) Lee, National Cheng Kung University, Taiwan
    Lawrence Holder, Washington State University, USA
    Vinod Tipparaju, AMD, USA
    Nishkam Ravi, NEC Labs, USA
    Renato Porfirio Ishii, Federal University of Mato Grosso do Sul (UFMS),
Brazil

Should you have any questions regarding the workshop or this webpage,
please contact parlearning ~AT~ googlegroups DOT com.
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