Call for Papers: The 5th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics

[Arindam Pal]

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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/