ParLearning 2016 - The 5th International Workshop on Parallel and
Distributed
Computing for Large Scale Machine Learning and Big Data Analytics
http://parlearning.ecs.fullerton.edu/
May 27, 2016
Chicago, USA
in conjunction with
The 30th IEEE International Parallel & Distributed Processing Symposium
(IPDPS 2016)
http://www.ipdps.org/
May 23-27, 2016
Chicago Hyatt Regency
Chicago, Illinois, USA
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Call for Papers
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.)
Keynote talk
Dr. Peter Kogge, University of Notre Dame
Organization
Charalampos Chelmis, University of Southern California, USA
Sutanay Choudhury, Pacific Northwest National Laboratory, USA
Arindam Pal, TCS Innovation Labs, India
Anand Panangadan, California State University, Fullerton, USA
Weiqin Tong, Shanghai University, China
Yinglong Xia, IBM T.J. Watson Research Center, USA
Program Committee
Jaume Bacardit, Newcastle University, UK
Danny Bickson, GraphLab Inc., USA
Zhihui Du, Tsinghua University, China
Ahmed Eldawy, University of Minnesota, USA
Dinesh Garg, IBM India Research Laboratory, India
Renato Porfirio Ishii, Federal University of Mato Grosso do Sul (UFMS),
Brazil
Ananth Kalyanaraman, Washington State University, USA
Joo-Young Kim, Microsoft Research, USA
Gwo Giun (Chris) Lee, National Cheng Kung University, Taiwan
Carson Leung, University of Manitoba, Canada
Arijit Mukherjee, TCS Innovation Labs, India
Debnath Mukherjee, TCS Innovation Labs, India
Francesco Parisi, University of Calabria, Italy
Himadri Sekhar Paul, TCS Innovation Labs, India
Chandan Reddy, Wayne State University, USA
Gautam Shroff, TCS Innovation Labs, India
Aniruddha Sinha, TCS Innovation Labs, India
Zhuang Wang, Facebook, USA
Naixue Xiong, Colorado Technical University, USA
Jianting Zhang, City College of New York, USA
Important Dates
Paper submission: January 15, 2016 AoE
Notification: February 12, 2016
Camera Ready: February 26, 2016
Paper Guidelines
Submitted manuscripts should be 6-10 single-spaced double-column pages
using 10-point size font on 8.5x11 inch pages (IEEE conference style),
including figures, tables, and references. Format requirements are posted
on the IEEE IPDPS web page.
All submissions must be uploaded electronically at
http://edas.info/newPaper.php?c=21782
Regards,
Dr. Arindam Pal
Research Scientist
Innovation Labs Kolkata
TCS Research
http://www.cse.iitd.ac.in/~arindamp/
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