[Esip-machinelearning] FW: [EXTERNAL] [SIG-IRList] CFP: ParLearning 2019 in conjunction with KDD 2019

[Mcgibbney, Lewis J (398M)]

Dr. Lewis John McGibbney Ph.D., B.Sc.(Hons)
Data Scientist III
Computer Science for Data Intensive Applications Group (398M)
Instrument Software and Science Data Systems Section (398)
Jet Propulsion Laboratory
California Institute of Technology
4800 Oak Grove Drive
Pasadena, California 91109-8099
Mail Stop : 158-256C
Tel:  (+1) (818)-393-7402
Cell: (+1) (626)-487-3476
Fax:  (+1) (818)-393-1190
Email: lewis.j.mcgibbney at jpl.nasa.gov<mailto:lewis.j.mcgibbney at jpl.nasa.gov>
ORCID: orcid.org/0000-0003-2185-928X

           [signature_963816144]

 Dare Mighty Things

From: ACM SIGIR Mailing List <SIGIR at LISTSERV.ACM.ORG> on behalf of Arindam Pal <arindamp at GMAIL.COM>
Reply-To: Arindam Pal <arindamp at GMAIL.COM>
Date: Tuesday, April 16, 2019 at 7:22 PM
To: "SIGIR at LISTSERV.ACM.ORG" <SIGIR at LISTSERV.ACM.ORG>
Subject: [EXTERNAL] [SIG-IRList] CFP: ParLearning 2019 in conjunction with KDD 2019

****************************************************************************************
* The 8th International Workshop on Parallel and Distributed Computing for
* Large-Scale Machine Learning and Big Data Analytics (ParLearning 2019)
* https://parlearning.github.io<https://parlearning.github.io/>
* August 5, 2019
* Anchorage, Alaska, USA
*
* Co-located with
* The 25th ACM SIGKDD International Conference on
* Knowledge Discovery and Data Mining (KDD 2019)
* https://www.kdd.org/kdd2019/
* August 4 - August 8, 2019
* Dena’ina Convention Center and William Egan Convention Center
* Anchorage, Alaska, 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 time of "Big Data". The past ten years have 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 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 should describe methods for scaling up X using Y on Z, where potential choices for X, Y and Z are provided below.

Scaling up

o Recommender systems
o Optimization algorithms (gradient descent, Newton methods)
o Deep learning
o Distributed algorithms and AI for Blockchain
o Sampling/sketching techniques
o Clustering (agglomerative techniques, graph clustering, clustering heterogeneous data)
o Classification (SVM and other classifiers)
o SVD and other matrix computations
o Probabilistic inference (Bayesian networks)
o Logical reasoning
o Graph algorithms, graph mining and knowledge graphs
o Semi-supervised learning
o Online and streaming learning
o Generative adversarial networks

Using

o Parallel architectures/frameworks (OpenMP, OpenCL, OpenACC, Intel TBB)
o Distributed systems/frameworks (GraphLab, Hadoop, MPI, Spark)
o Machine learning frameworks (TensorFlow, PyTorch, Theano, Caffe)

On

o Clusters of conventional CPUs
o Many-core CPU (e.g. Xeon Phi)
o FPGA
o Specialized ML accelerators (e.g. GPU and TPU)

Workshop Proceedings

Accepted papers will be published in the conference proceedings by ACM and also appear in the ACM Digital Library.

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 ACM KDD 2019 web page.

Important Dates

o Paper submission: May 5, 2019 (Anywhere on Earth)
o Author notification: June 1, 2019
o Camera-ready version: June 8, 2019

Paper Guidelines

Submissions are limited to a total of 10 pages, including all content and references, and must be in PDF format and formatted according to the new Standard ACM Conference Proceedings Template. Additional information about formatting and style files is available online at: https://www.acm.org/publications/proceedings-template. Papers that do not meet the formatting requirements will be rejected without review.
All submissions must be uploaded electronically at https://www.easychair.org/conferences/?conf=parlearning2019.

Keynote Speakers

o Professor V.S. Subrahmanian (Dartmouth College, Hanover, NH, USA)

Organizing Committee

o General Chairs: Arindam Pal (TCS Research and Innovation, Kolkata, India) and Henri Bal (Vrije Universiteit, Amsterdam, Netherlands)
o Program Chairs: Azalia Mirhoseini (Google AI, Mountain View, CA, USA), Thomas Parnell (IBM Research, Zurich, Switzerland)
o Publicity Chair: Anand Panangadan (California State University, Fullerton, USA)
o Steering Committee Chairs: Sutanay Choudhury (Pacific Northwest National Laboratory, Richland, WA, USA) and Yinglong Xia (Huawei Research America, Santa Clara, CA, USA)

Regards,
Arindam Pal, Ph.D.
Research Scientist
TCS Research and Innovation
http://www.cse.iitd.ac.in/~arindamp/
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