Workshop on Parallel and Distributed Computing for Machine Learning and
Inference Problems (ParLearning'2013)

http://cass-mt.pnnl.gov/parlearning.aspx

May 24, 2013, Boston, Massachusetts USA. To be held in conjunction with
IPDPS 2013 (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Authors are invited to submit manuscripts of original unpublished research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
computingtechniques 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
      - 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
      - Apache Mahout, Apache Giraph, IBM Parallel Learning Toolbox,
      GraphLab etc.
      - Domain-specific languages for Parallel Computation
      - GPU-integration for Java/Python

Important Days:
    January 13, 2013       Submission of manuscripts
    February 4, 2013       Notification of decision
    February 28, 2013     Submission of camera-ready papers

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. The templates are available here: for
LaTex and Word. All papers must be submitted through the EDAS portal

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

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

Program Committee

    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
    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
    Yi Wang, Tencent Holdings, China
    Yihua Huang, Nanjing University, China