Connectionists: MPP 2020: 9th Workshop on Parallel Programming Models - Special Edition on IoT, Edge/Fog computing: Machine Learning and Security

[Felipe Maia Galvao Franca]

MPP 2020: 9th Workshop on Parallel Programming Models - Special Edition on
IoT, Edge/Fog computing: Machine Learning and Security
Hilton New Orleans Riverside
New Orleans, LA, United States, May 19-22, 2020

Conference website http://www.mpp-conf.org/
Submission link https://easychair.org/conferences/?conf=mpp2020
Abstract registration deadline February 18, 2020
Submission deadline February 21, 2020
Topics: machine learning security iot parallel computing

Recent publications show that, since it is clear that machine learning has
become ubiquitous, it is necessary to produce research to increase
currently ML algorithms' performance, power consumption and, most
importantly as of recently, security. The performance issues are quite
clear to understand and have been addressed by multiple researchers either
with new algorithms for model compression/parallelization or with new
specific hardware. Roughly speaking, it is possible to categorize the
security threats against ML models in privacy leakage and model evasion
("fooling" the system into making wrong decisions). This edition of the
Workshop on Parallel Programming Models (MPP) is focused on addressing
theses issues which are of the utmost importance for the Machine Learning
community at this point.

When addressing the performance aspect of Machine Learning, there is also
the issue of the amount of data used for training deep-learning models. In
the case of Big Data, the application of in-memory computing (which was the
main topic in MPP 2019) can be essential to reduce the gap between data and
the ML model, in terms of latency.

MPP aims at bringing together researchers interested in presenting
contributions to the evolution of existing models or in proposing novel
ones, considering the trends on Machine Learning, In-Memory Computing and
Security. MPP 2020 will be held in conjunction with The 34th IEEE
International Parallel and Distributed Processing Symposium (IPDPS 2020),
in New Orleans, Louisiana, USA, on May 22, 2020.



List of Topics

Topics of interest include (but are not limited to):

Compression of Deep-Learning Models;
Tools for ML Model design;
Hardware specifically designed for Machine Learning;
In-Memory Computing;
Novel Deep Neural Networks architectures;
Error Detection/Recovery in ML systems;
Robust Neural Networks;
Privacy of data in ML systems;
Robustness of decision making ML systems;
Neural networks inference and training on IoT, Fog, Edge and cloud
environments;
Machine Learning for Parallel Applications and IoT.




Submission Guidelines
MPP invites authors to submit unpublished full and short papers on the
subjects. Submissions must be in English, 8 pages maximum for full papers
and 4 pages for short papers, following the IEEE formatting guidelines.
Page limits include references. Papers must be submitted by Feb, 18, 2020,
in the following url: https://easychair.org/conferences/?conf=mpp2020

Committees
Program Committee
Tiago Alves (State University of Rio de Janeiro)
Luciana Arantes (Universite Pierre et Marie Curie-Paris6)
Cristiana Bentes (State University of Rio de Janeiro)
Cristina Boeres (Fluminense Federal University)
Maria Clicia Castro (State University of Rio de Janeiro)
Lúcia Drummond (Fluminense Federal University)
Felipe M.G. França (COPPE-UFRJ)
Roberto Giorgi ( University of Siena)
Rekai Gonzalez-Alberquilla
Dalvan Griebler (PUCRS/SETREM)
Konstantinos  Krommydas (Intel)
Nithesh Kurella (AMD)
Arthur Lorenzon (Federal University of Pampa)
Leandro A. J. Marzulo (Google)
Gabriele Mencagli (University of Pisa)
Elias Mizan (Esperanto Technologies)
Edward David Moreno (UFS - Federal University of Sergipe)
Alexandre Nery (Universidade de Brasília)
Carla Osthoff Barros (National Laboratory for Scientific Computing LNCC)
Mauricio Pilla (Google)
Andrew Putnam
Alexandre Sena (State University of Rio de Janeiro)
Silvio Stanzani  (Universidade Estadual Paulista - UNESP)
Kazutomo Yoshii (Argonne National Laboratory)

Contact
All questions about submissions should be emailed to tiago at ime.uerj.br

-- 
–––––––––––––––––––––––––––––––
Felipe M. G. França, PhD
Invited Professor of Computer Science and Engineering
Systems Engineering and Computer Science Program, COPPE
Universidade Federal do Rio de Janeiro
P.O. Box 68511, 21941-972, Rio de Janeiro, RJ, Brazil
felipe at ieee.org
felipe at cos.ufrj.br
–––––––––––––––––––––––––––––––
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