Final Call For Papers: ParLearning 2017
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Arindam Pal
Jan 13, 2017, 3:56:34 AM1/13/17
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The 6th International Workshop on Parallel and Distributed Computing
for Large Scale Machine Learning and Big Data Analytics
http://parlearning.ecs.fullerton.edu/
May 29, 2017
In Conjunction with
31st IEEE International Parallel & Distributed Processing Symposium
May 29 - June 2, 2017
Buena Vista Palace Hotel
Orlando, Florida 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 parallel and distributed computing, several frameworks such as OpenMP, OpenCL, 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
Multi-core architectures/frameworks (OpenMP)
Many-core (GPU) architectures/frameworks (OpenCL, OpenACC, CUDA, Intel TBB)
Distributed systems/frameworks (GraphLab, MPI, Hadoop, Spark, Storm, Mahout etc.)
Proceedings of the ParLearning workshop will be distributed at the conference and will be submitted for inclusion in the IEEE Xplore Digital Library after the conference.
Journal publication
Selected papers from the workshop will be published in a Special Issue of Future Generation Computer Systems, Elsevier's International Journal of eScience. Special Issue papers will undergo additional review.
Awards
Best Paper Award: The program committee will nominate a paper for the Best Paper award. In past years, the Best Paper award included a cash prize. Stay tuned for this year!
Travel awards:Students with accepted papers have a chance to apply for a travel award. Please find details on the IEEE IPDPS web page.
Organization
General chair: Anand Panangadan (California State University, Fullerton, USA)
Technical Program co-chairs: Henri Bal (Vrije Universiteit, The Netherlands) and Arindam Pal (TCS Research, India)
Publicity chair: Charalampos Chelmis (University at Albany, State University of New York, USA)
Steering Committee chair: Yinglong Xia (Huawei Research, USA)
Technical Program Committee
Brojeshwar Bhowmick, TCS Research, India
Danny Bickson, GraphLab Inc., USA
Vito Giovanni Castellana, Pacific Northwest National Laboratory, USA
Tanushyam Chattopadhyay, TCS Research, India
Daniel Gerardo Chavarria, Pacific Northwest National Laboratory, USA
Sutanay Choudhury, Pacific Northwest National Laboratory, USA
Valeriu Codreanu, SURFsara, The Netherlands
Lipika Dey, TCS Research, India
Zhihui Du, Tsinghua University, China
Anand Eldawy, University of Minnesota, USA
Dinesh Garg, IBM Research, India
Saptarshi Ghosh, IIEST Shibpur, India
Dianwei Han, Northwestern University, USA
Renato Porfirio Ishii, Federal University of Mato Grosso do Sul (UFMS), Brazil
Ananth Kalyanaraman, Washington State University, USA
Gwo Giun (Chris) Lee, National Cheng Kung University, Taiwan
Carson Leung, University of Manitoba, Canada
Animesh Mukherjee, IIT Kharagpur, India
Debnath Mukherjee, TCS Research, India
Francesco Parisi, University of Calabria, Italy
Himadri Sekhar Paul, TCS Research, India
Aske Plaat, Leiden University, The Netherlands
Chandan Reddy, Wayne State University, USA
Rekha Singhal, TCS Research, India
Weiqin Tong, Shanghai University, China
Cedric van Nugteren, TomTom International BV
Zhuang Wang, Facebook, USA
Qingsong Wen, Georgia Institute of Technology, USA
Bo Zhang, IBM, USA
Jianting Zhang, City College of New York, USA
Important Dates
Paper submission: January 20, 2017 AoE
Notification: February 10, 2017
Camera Ready: March 10, 2017
Paper Guidelines
Submitted manuscripts should be upto 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 https://www.easychair.org/conferences/?conf=parlearning2017
The 6th International Workshop on Parallel and Distributed Computing
for Large Scale Machine Learning and Big Data Analytics
http://parlearning.ecs.fullerton.edu/
May 29, 2017
In Conjunction with
31st IEEE International Parallel & Distributed Processing Symposium
May 29 - June 2, 2017
Buena Vista Palace Hotel
Orlando, Florida 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 parallel and distributed computing, several frameworks such as OpenMP, OpenCL, 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
Multi-core architectures/frameworks (OpenMP)
Many-core (GPU) architectures/frameworks (OpenCL, OpenACC, CUDA, Intel TBB)
Distributed systems/frameworks (GraphLab, MPI, Hadoop, Spark, Storm, Mahout etc.)
Proceedings of the ParLearning workshop will be distributed at the conference and will be submitted for inclusion in the IEEE Xplore Digital Library after the conference.
Journal publication
Selected papers from the workshop will be published in a Special Issue of Future Generation Computer Systems, Elsevier's International Journal of eScience. Special Issue papers will undergo additional review.
Awards
Best Paper Award: The program committee will nominate a paper for the Best Paper award. In past years, the Best Paper award included a cash prize. Stay tuned for this year!
Travel awards:Students with accepted papers have a chance to apply for a travel award. Please find details on the IEEE IPDPS web page.
Organization
General chair: Anand Panangadan (California State University, Fullerton, USA)
Technical Program co-chairs: Henri Bal (Vrije Universiteit, The Netherlands) and Arindam Pal (TCS Research, India)
Publicity chair: Charalampos Chelmis (University at Albany, State University of New York, USA)
Steering Committee chair: Yinglong Xia (Huawei Research, USA)
Technical Program Committee
Brojeshwar Bhowmick, TCS Research, India
Danny Bickson, GraphLab Inc., USA
Vito Giovanni Castellana, Pacific Northwest National Laboratory, USA
Tanushyam Chattopadhyay, TCS Research, India
Daniel Gerardo Chavarria, Pacific Northwest National Laboratory, USA
Sutanay Choudhury, Pacific Northwest National Laboratory, USA
Valeriu Codreanu, SURFsara, The Netherlands
Lipika Dey, TCS Research, India
Zhihui Du, Tsinghua University, China
Anand Eldawy, University of Minnesota, USA
Dinesh Garg, IBM Research, India
Saptarshi Ghosh, IIEST Shibpur, India
Dianwei Han, Northwestern University, USA
Renato Porfirio Ishii, Federal University of Mato Grosso do Sul (UFMS), Brazil
Ananth Kalyanaraman, Washington State University, USA
Gwo Giun (Chris) Lee, National Cheng Kung University, Taiwan
Carson Leung, University of Manitoba, Canada
Animesh Mukherjee, IIT Kharagpur, India
Debnath Mukherjee, TCS Research, India
Francesco Parisi, University of Calabria, Italy
Himadri Sekhar Paul, TCS Research, India
Aske Plaat, Leiden University, The Netherlands
Chandan Reddy, Wayne State University, USA
Rekha Singhal, TCS Research, India
Weiqin Tong, Shanghai University, China
Cedric van Nugteren, TomTom International BV
Zhuang Wang, Facebook, USA
Qingsong Wen, Georgia Institute of Technology, USA
Bo Zhang, IBM, USA
Jianting Zhang, City College of New York, USA
Important Dates
Paper submission: January 20, 2017 AoE
Notification: February 10, 2017
Camera Ready: March 10, 2017
Paper Guidelines
Submitted manuscripts should be upto 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 https://www.easychair.org/conferences/?conf=parlearning2017
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