CALL FOR PAPERS
******************************
The 4th International Workshop on
Parallel and Distributed Computing for
Large Scale Machine Learning and Big Data Analytics
******************************
May 29, 2015
Hyderabad, India
In Conjunction with IPDPS 2015
http://www.usc.edu/dept/
******************************
Scaling up machine-learning (ML), data mining (DM) and reasoning
algorithms from Artificial Intelligence (AI) for massive datasets
is a major technical challenge in the times of "Big Data". The
past ten years has seen the rise of multi-core and GPU based computing.
In distributed computing, several frameworks such as Mahout, GraphLab
and Spark continue to appear to facilitate scaling up ML/DM/AI
algorithms using higher levels of abstraction. We invite novel works
that advance the trio-fields of ML/DM/AI through development of
scalable algorithms or computing frameworks. Ideal submissions would
be characterized as scaling up X on Y, where potential choices for
X and Y are provided below.
Scaling up
---- recommender systems
---- gradient descent algorithms
---- deep learning
---- sampling/sketching techniques
---- clustering (agglomerative techniques, graph clustering,
clustering heterogeneous data)
---- classification (SVM and other classifiers)
---- SVD
---- probabilistic inference (bayesian networks)
---- logical reasoning
---- graph algorithms and graph mining
On
---- Parallel architectures/frameworks (OpenMP, OpenCL, Intel TBB)
---- Distributed systems/frameworks (GraphLab, Hadoop, MPI, Spark etc.)
==============================
ORGANIZATION
==============================
Sutanay Choudhury, Pacific Northwest National Laboratory, USA
Arindam Pal, TCS Innovation Labs, India
Anand Panangadan, University of Southern California, USA
Yinglong Xia, IBM Research, USA
==============================
PROGRAM COMMITTEE
==============================
Virendra C. Bhavsar, University of New Brunswick, Canada
Danny Bickson, GraphLab Inc., USA
Peter Boncz, Vrije Universiteit, Netherlands
Zhihui Du, Tsinghua University, China
Dinesh Garg, IBM India Research Laboratory, India
Qirong Ho, Infocomm Research, A*STAR, Singapore
Yihua Huang, Nanjing University, China
Renato Porfirio Ishii, Federal University of Mato
Grosso do Sul (UFMS), Brazil
Ananth Kalyanaraman, Washington State University, USA
Dionysis Logothetis, Telefonica, Spain
Debnath Mukherjee, TCS Innovation Labs, India
Huansheng Ning, Beihang University, China
Gautam Shroff, TCS Innovation Labs, India
Aniruddha Sinha, TCS Research, India
Neal Xiong, Georgia State University, USA
Jianting Zhang, City College of New York, USA
Wei Zhang, IBM Research, USA
==============================
IMPORTANT DATES
==============================
Paper submission: January 18th, 2015 AON
Notification: February 14th, 2015
Camera Ready: February 28th, 2015
==============================
PAPER GUIDELINES
==============================
Submitted manuscripts may not exceed 6 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) after
the author notification.
==============================
PUBLICATIONS AND AWARDS
==============================
---- The workshop proceedings will be added to ACM
Digital Library.
---- A best paper award, sponsored by Pacific Northwest
National Laboratory, USA will be announced at the
workshop.
---- Accepted papers with sufficient extensions will be
recommended for publication in a journal (TBD), subject
to review by the journal editorial board.
---- Students with accepted papers have a chance to apply
for a travel award. Please find details at www.ipdps.org.
==============================
PREVIOUS PARLEARNING WORKSHOPS
==============================
2012 - http://researcher.watson.ibm.
2013 - http://cass-mt.pnnl.gov/
2014 - https://edas.info/web/
******************************
The 4th International Workshop on
Parallel and Distributed Computing for
Large Scale Machine Learning and Big Data Analytics
******************************
May 29, 2015
Hyderabad, India
In Conjunction with IPDPS 2015
http://www.usc.edu/dept/
******************************
Scaling up machine-learning (ML), data mining (DM) and reasoning
algorithms from Artificial Intelligence (AI) for massive datasets
is a major technical challenge in the times of "Big Data". The
past ten years has seen the rise of multi-core and GPU based computing.
In distributed computing, several frameworks such as Mahout, GraphLab
and Spark continue to appear to facilitate scaling up ML/DM/AI
algorithms using higher levels of abstraction. We invite novel works
that advance the trio-fields of ML/DM/AI through development of
scalable algorithms or computing frameworks. Ideal submissions would
be characterized as scaling up X on Y, where potential choices for
X and Y are provided below.
Scaling up
---- recommender systems
---- gradient descent algorithms
---- deep learning
---- sampling/sketching techniques
---- clustering (agglomerative techniques, graph clustering,
clustering heterogeneous data)
---- classification (SVM and other classifiers)
---- SVD
---- probabilistic inference (bayesian networks)
---- logical reasoning
---- graph algorithms and graph mining
On
---- Parallel architectures/frameworks (OpenMP, OpenCL, Intel TBB)
---- Distributed systems/frameworks (GraphLab, Hadoop, MPI, Spark etc.)
==============================
ORGANIZATION
==============================
Sutanay Choudhury, Pacific Northwest National Laboratory, USA
Arindam Pal, TCS Innovation Labs, India
Anand Panangadan, University of Southern California, USA
Yinglong Xia, IBM Research, USA
==============================
PROGRAM COMMITTEE
==============================
Virendra C. Bhavsar, University of New Brunswick, Canada
Danny Bickson, GraphLab Inc., USA
Peter Boncz, Vrije Universiteit, Netherlands
Zhihui Du, Tsinghua University, China
Dinesh Garg, IBM India Research Laboratory, India
Qirong Ho, Infocomm Research, A*STAR, Singapore
Yihua Huang, Nanjing University, China
Renato Porfirio Ishii, Federal University of Mato
Grosso do Sul (UFMS), Brazil
Ananth Kalyanaraman, Washington State University, USA
Dionysis Logothetis, Telefonica, Spain
Debnath Mukherjee, TCS Innovation Labs, India
Huansheng Ning, Beihang University, China
Gautam Shroff, TCS Innovation Labs, India
Aniruddha Sinha, TCS Research, India
Neal Xiong, Georgia State University, USA
Jianting Zhang, City College of New York, USA
Wei Zhang, IBM Research, USA
==============================
IMPORTANT DATES
==============================
Paper submission: January 18th, 2015 AON
Notification: February 14th, 2015
Camera Ready: February 28th, 2015
==============================
PAPER GUIDELINES
==============================
Submitted manuscripts may not exceed 6 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) after
the author notification.
==============================
PUBLICATIONS AND AWARDS
==============================
---- The workshop proceedings will be added to ACM
Digital Library.
---- A best paper award, sponsored by Pacific Northwest
National Laboratory, USA will be announced at the
workshop.
---- Accepted papers with sufficient extensions will be
recommended for publication in a journal (TBD), subject
to review by the journal editorial board.
---- Students with accepted papers have a chance to apply
for a travel award. Please find details at www.ipdps.org.
==============================
PREVIOUS PARLEARNING WORKSHOPS
==============================
2012 - http://researcher.watson.ibm.
2013 - http://cass-mt.pnnl.gov/
2014 - https://edas.info/web/
******************************
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