[Tccc] [Call-for-Participation] ParLearning 2013, Boston, USA

Yinglong Xia yinglongxiaatgmail.com
Mon May 20 17:54:03 EDT 2013



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            Co-Located with IPDPS 2013, Boston, USA

                    ParLearning 2013
              2nd International Workshop on
            Parallel and Distributed Computing for
            Machine Learning and Inference Problems
                      May 24, 2013

         http://cass-mt.pnnl.gov/parlearning.aspx
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* KEYNOTES:
- Prof. Srinivasan Parthasarathy, Ohio State University, USA:
        Large Scale Data Analytics: Challenges, and the role
        of Stratified Data Placement.
- Prof. Srinivas Aluru, Iowa State University, USA
        Parallel Methods for Bayesian Network Structure Learning.

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*  PROGRAM
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Morning session 1 (08:30-09:40)

8:30-8:40 Opening Remarks
8:40-9:40 Keynote: Prof. Srinivasan Parthasarathy.
          Large Scale Data Analytics: Challenges, and the role of
          Stratified Data Placement.

9:40-10:00 Coffee break
Morning session 2 (10:00-12:00)

 -  Combining parallel algorithms solving the same application:
    What is the best approach? Alfredo Goldman, Joachim Lepping,
    Yanik Ngoko, and Denis Trystram.
 -  Enhancing Accuracy and Performance of Collaborative Filtering
    Algorithm by Stochastic SVD and Its MapReduce Implementation.
    Che-Rung Lee and Ya-Fang Chang.
 -  Reducing False Transactional Conflicts With Speculative
    Sub-blocking State - An Empirical Study for ASF Transactional
    Memory System. Lifeng Nai and Hsien-Hsin Lee.
 -  Revisiting a pattern for processing combinatorial objects in
    parallel. Christian Trefftz and Jerry Scipps.

Lunch (12:00-13:00)

Afternoon session 1 (13:00-15:00)

13:00-14:00 Keynote: Prof.Srinivas Aluru.
        Parallel Methods for Bayesian Network Structure Learning.
 -  EDA and ML - A Perfect Pair for Large-Scale Data Analysis.
    Ryan Hafen and Terence Critchlow.
 -  Combining Structure and Property Values is Essential for
    Graph-based Learning. David J. Haglin and Larry Holder.
 -  Concluding Remarks

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Topics of Interest
------------------------------------------------------------------
 -  Learning and inference using large scale Bayesian Networks
 -  Scaling up frequent subgraph mining or other graph pattern
    mining techniques
 -  Scalable implementations of learning algorithms for massive
    sparse datasets
 -  Scalable clustering of massive graphs or graph streams
 -  Scalable algorithms for topic modeling
    HPC enabled approaches for emerging trend detection in social
    media
 -  Comparison of various HPC infrastructures for learning
 -  GPU-accelerated implementations for topic modeling or other
    text mining problems
 -  Knowledge discovery from scientific applications with massive
    datasets (climate, systems biology etc.)
 -  Performance analysis of key machine-learning algorithms from
    newer parallel and distributed computing frameworks such as
    Mahout, Giraph, IBM Parallel Learning Toolbox, GraphLab etc.
    Domain-specific languages for Parallel Computation

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Organizers
------------------------------------------------------------------
Program Chair:
    Chandrika Kamath, Lawrence Livermore National Laboratory, USA
    Roger Barga, Microsoft Research, USA

Workshop Co-Chairs:
    Yinglong Xia, IBM Research, USA
    Sutanay Choudhury, Pacific Northwest National Laboratory, USA
    George Chin, Pacific Northwest National Laboratory, USA

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Program Committee
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    Anne Hee Hiong Ngu, Texas State University, USA
    Anuj Shah, Netflix, USA
    Arindam Pal, Indian Institute of Technology, India
    Avery Ching, Facebook, USA
    Benjamin Herta, IBM Research, USA
    Ghaleb Abdulla, Lawrence Livermore National Laboratory, USA
    James Montgomery, Australian National University, Australia
    Lawrence Holder, Washington State University, USA
    Liu Peng, Microsoft, USA
    Mahantesh Halappanavar, Pacific Northwest National Laboratory, USA
    Mladen Vouk, North Carolina State University, USA
    Oreste Villa, Pacific Northwest National Laboratory, USA
    Simon Kahan, University of Washington, USA
    Yangqiu Song, Microsoft Research, China
    Yaohang Li, Old Dominion University, USA
    Yihua Huang, Nanjing University, USA
    Yi Wang, Tencent Holdings, China
_______________________________________________
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