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ParLearning 2016 : The 5th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics


When May 27, 2016 - May 27, 2016
Where Chicago, Illinois, USA
Submission Deadline Jan 22, 2016
Notification Due Feb 12, 2016
Final Version Due Feb 26, 2016
Categories    data mining   machine learning   artificial intelligence   parallel algorithms

Call For Papers

ParLearning 2016 - The 5th International Workshop on Parallel and Distributed
Computing for Large Scale Machine Learning and Big Data Analytics
May 27, 2016
Chicago, USA

in conjunction with
The 30th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2016)
May 23-27, 2016
Chicago Hyatt Regency
Chicago, Illinois, USA

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)
probabilistic inference (bayesian networks)
logical reasoning
graph algorithms and graph mining


Parallel architectures/frameworks (OpenMP, OpenCL, Intel TBB)
Distributed systems/frameworks (GraphLab, Hadoop, MPI, Spark etc.)

Keynote talk

Dr. Peter Kogge, University of Notre Dame

Organizing Committee

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
Najjar Walid, University of California, Riverside
Zhuang Wang, Facebook, USA
Naixue Xiong, Colorado Technical University, USA
Jianting Zhang, City College of New York, USA

Important Dates

Paper submission: January 22, 2016 AoE
Notification: February 12, 2016
Camera Ready: February 26, 2016

Paper Guidelines

Submitted manuscripts may not exceed 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

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