[ecoop-info] MOD2017: Paper submission deadline: May 15, 2017

Giuseppe Nicosia nicosia at dmi.unict.it
Sat May 6 17:45:26 CEST 2017

*Our apologies if you receive multiple copies of this announcement.*

MOD 2017: The 3rd International Conference on Machine learning,
Optimization & big Data
An Interdisciplinary Conference: Machine Learning, Optimization and Data
Science without Borders
September 14 - 17, 2017, Volterra (Pisa) Tuscany, Italy

Important dates
* Full Paper Submissions: May 15, 2017
* Full Paper  Notifications: June 30, 2017
* Conference: September 14 - 17, 2017

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.

Keynote Speakers
+ Yi-Ke Guo, Department of Computing, Faculty of Engineering, Imperial
College London, UK Founding Director of Data Science Institute.

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

+ Jun Pei, Hefei University of Technology, China

+ Georgios Giannakis, Department of Electrical and Computer Engineering,
University of Minnesota, Director of Digital Technology Center, USA (TBC)

Special Sessions
+ "Metaheuristics and Multi-Objective optimization for Big Data"
Clarisse Dhaenens, University of Lille, France
Laetitia Jourdan, University of Lille, France

+ “Industrial Session on Machine Learning, Optimization and Data Science
for Real-World Applications”
Ilaria Bordino, Marco Firrincieli, Fabio Fumarola, and Francesco Gullo,
UniCredit R&D, Italy

+ “Tutorial on Scalable Data Mining on Cloud Computing Systems”
Domenico Talia, University of Calabria, Italy

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
* 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
* 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
* Big Data mining for cyber-physical systems and complex, time-evolving
* 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:
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.


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.

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.

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

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

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

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

Giuseppe Nicosia, Ph.D.
Associate Professor of Computer Science
Dept. of Mathematics & Computer Science
University of Catania
Viale A. Doria, 6  - 95125 Catania, Italy
P +39 095 7383048
nicosia at dmi.unict.it
4th International Synthetic & Systems Biology Summer School - SSBSS 2017
* Biology meets Computer Science & Engineering *
July 17-21, 2017 - University of Cambridge, Robinson College, UK
Contact Email: ssbss.school at gmail.com

FB: https://www.facebook.com/ssbss.school/

SSBSS - Synthetic & Systems Biology Summer School Group:

Computational Synthetic Biology Group:
3rd International Conference on Machine learning, Optimization & big Data -
MOD 2017
An Interdisciplinary Conference: Machine Learning, Optimization and Data
Science without Borders
September 14-17, 2017 - Volterra (Pisa), Tuscany, Italy
modworkshop2017 at gmail.com

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