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MOD 2017 : 3rd International Conference on Machine learning, Optimization & big Data - An Interdisciplinary Conference: Machine Learning, Optimization and Data Science without Borders

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Link: http://www.taosciences.it/mod/
 
When Sep 14, 2017 - Sep 17, 2017
Where Volterra (Pisa) Tuscany
Abstract Registration Due Jun 15, 2017
Submission Deadline Mar 31, 2017
Notification Due May 1, 2017
Final Version Due Jun 1, 2017
Categories    machine learning   big data   data science   optimization
 

Call For Papers

Important dates
===============
* Full Paper Submissions: March 31, 2017
* Full Paper Notifications: May 1, 2017
* Special Session Proposals: March 31, 2017
* Special Session Notifications: April 15, 2017
* Workshop Proposals: March 31, 2017
* Workshop Notifications: April 15, 2017
* Conference: September 14 - 17, 2017

The International Conference on Machine learning, Optimization, and big Data (MOD) has established itself as a premier interdisciplinary conference
in machine learning, computational optimization, knowledge discovery and data science. It provides an international forum for presentation of original
multidisciplinary research results, as well as exchange and dissemination of innovative and practical development experiences.

The conference will consist of four days of conference sessions. We invite submissions of papers on all topics related to Machine learning, Optimization,
Knowledge Discovery and Data Science including real-world applications for the Conference Proceedings (Springer ? Lecture Notes in Computer Science -LNCS).

Topics of Interest
The last five-year period has seen a impressive revolution in the theory and application of machine learning, optimization and big data.

Topics of interest include, but are not limited to:
* Foundations, algorithms, models and theory of data science, including big data mining.
* Machine learning and statistical methods for big data.
* Machine Learning algorithms and models. Neural Networks and Learning Systems. Convolutional neural networks.
* Unsupervised, semi-supervised, and supervised Learning.
* Knowledge Discovery. Learning Representations. Representation learning for planning and reinforcement learning.
* Metric learning and kernel learning. Sparse coding and dimensionality expansion. Hierarchical models. Learning representations of outputs or states.
* Multi-objective optimization. Optimization and Game Theory. Surrogate-assisted Optimization. Derivative-free Optimization.
* Big data Mining from heterogeneous data sources, including text, semi-structured, spatio-temporal, streaming, graph, web, and multimedia data.
* Big Data mining systems and platforms, and their efficiency, scalability, security and privacy.
* Computational optimization. Optimization for representation learning. Optimization under Uncertainty
* Optimization algorithms for Real World Applications. Optimization for Big Data. Optimization and Machine Learning.
* Implementation issues, parallelization, software platforms, hardware
* Big Data mining for modeling, visualization, personalization, and recommendation.
* Big Data mining for cyber-physical systems and complex, time-evolving networks.
* Applications in social sciences, physical sciences, engineering, life sciences, web, marketing, finance, precision medicine, health informatics, medicine and other domains.

We particularly encourage submissions in emerging topics of high importance such as data quality, advanced deep learning, time-evolving networks, large multi-objective optimization, quantum discrete optimization, learning representations, big data mining and analytics, cyber-physical systems, heterogeneous data integration and mining, autonomous decision and adaptive control.


Submission Guidelines
===============
Paper submissions should be limited to a maximum of 12 pages, in the Springer LNCS format:
http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0
including the bibliography and any possible appendices.

All submissions will be 6-blind reviewed by the Program Committee on the basis of technical quality, significance, multidisciplinary, relevance to scope of the conference, originality and clarity.

https://easychair.org/conferences/?conf=mod2017


Types of Submissions
===============
When submitting a paper to MOD 2017, authors are required to select
one of the following four types of papers:
+ Long paper: original novel and unpublished work (max. 12 pages in Springer LNCS format);
+ Short paper: an extended abstract of novel work (max. 4 pages);
+ Work for oral presentation only (no page restriction; any format).
For example, work already published elsewhere, which is relevant and which may solicit fruitful discussion at the conference;
+ Abstract for poster presentation only (max. 2 pages). The poster format for the presentation is A0 (118.9 cm high and 84.1 cm wide, respectively 46.8 x 33.1 inch). For research work which is relevant and which may solicit fruitful discussion at the conference.

Post-Proceedings
===============
All accepted long papers will be published in a volume of the series 'Lecture Notes in Computer Science' from Springer *after* the conference. Instructions for preparing and submitting the final versions (camera-ready papers) of all accepted papers will be available later on. All the other papers (short papers, abstracts of the oral presentations, abstracts for the poster presentations) will be published on the MOD 2017 web site.


Presentation
===============
MOD uses the single session formula of 30 minutes presentations for fruitful exchanges between authors and participants.


Best Paper Awards
===============
Springer sponsors the MOD 2017 Best Paper Award with a cash prize of EUR 1,000.
The Award will be conferred at the conference on the authors of the best paper award.


Attendance
===============
MOD is a premier forum for presenting and discussing current research in machine learning, optimization and big data. Therefore, at least one author of each accepted paper must complete the conference registration and present the paper at the conference, in order for the paper to be included in the proceedings and conference program.


Keynote Speakers
===============
+ Ruslan Salakhutdinov, Machine Learning Department, School of Computer Science at Carnegie Mellon University, USA. Director of AI Research at Apple.

Other speakers will be announced soon!


Organization
===============
General Chair:
Renato Umeton, Harvard University, USA

Program Co-Chairs:
Giovanni Giuffrida, University of Catania, Italy & Neodata Group
Giuseppe Nicosia, University of Catania, Italy
Panos Pardalos, University of Florida, USA

Special Session Co-Chairs:
Giuseppe Narzisi, New York University Tandon School of Engineering & New York Genome Center, New York, USA

Workshop Co-Chair:
Piero Conca, CNR, Italy

Industrial Panel Chairs:
Ilaria Bordino, Marco Firrincieli, Fabio Fumarola, and Francesco Gullo, UniCredit R&D

Publicity Chair:
Giovanni Murabito, DiGi Apps Inc.

W: http://www.taosciences.it/mod/
E: modworkshop2017@gmail.com

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