*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
* Late-Breaking Abstracts Submissions: July 15, 2017
Late-Breaking Abstracts
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Two-page abstracts describing late-breaking developments in the field of Machine Learning, Optimization and Data Science are solicited for presentation at the Late-Breaking Abstracts Workshop of the Machine learning Optimization and big Data (MOD 2017), and for inclusion in the proceedings companion to be published on the MOD 2017 web site.
*Presentation Format*
Following the success of the last years poster format for Late Breaking Abstracts, authors of the accepted submissions will be asked to prepare a poster summarizing their contributions. The chair will introduce each work at the beginning of the session and attendees will have the opportunity to interact with authors and enjoy a dynamic forum to share and spread scientific ideas. The details about the poster preparation will be sent to the authors of accepted abstracts.
*Selection Process*
Late-breaking abstracts will be briefly examined for relevance and minimum standards of acceptability, but will not be peer reviewed in detail. Authors of accepted late-breaking abstracts will individually retain copyright (and all other rights) to their late-breaking abstracts. Accepted late breaking abstracts with no author registered by the deadline will not appear in the Late-Breaking Abstracts section on the MOD 2017 web site.
*How to Submit an Abstract*
Submission deadline: Friday, July 15, 2017, 23:59 (Anywhere on Earth)
Page limit: 2 pages.
Author agreement: By submitting an abstract, the author(s) agree that, if their paper is accepted, they will:
+Register at least one author to attend the conference (by August 14, 2017)
+Attend the conference (at least one author) and present the accepted abstract at the conference.
Best Paper Award
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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
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+ “Assimilated Learning: A Framework for Co-analysis of Big Data and Smart Data”
Yi-Ke Guo, Department of Computing, Faculty of Engineering, Imperial College London, UK Founding Director of Data Science Institute.
+ “Quantification of Network Dissimilarities and its Practical Implications”
Panos Pardalos, Department of Systems Engineering, University of Florida, USA. Director of the Center for Applied Optimization.
+ “Recent Advances in Deep Learning”
Ruslan Salakhutdinov, Machine Learning Department, School of Computer Science at Carnegie Mellon University, USA. Director of AI Research at Apple.
+ “Socialize Strategies for Bots: when incomplete topology meets efficiency”
My Thai, Department of Computer & Information Science & Engineering, University of Florida, USA.
+ “Optimization and Management in Manufacturing Engineering”
Jun Pei, Hefei University of Technology, China
Tutorials
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+ “Tutorial on Scalable Data Mining on Cloud Computing Systems”
Domenico Talia, University of Calabria, Italy
+ “Tutorial on Mathematical Analysis of Nature-Inspired Algorithms”
Xin–She Yang, School of Science and Technology, Middlesex University London, United Kingdom
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.
Attendance
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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.
Organization
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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 Chair:
Giuseppe Narzisi, New York University Tandon School of Engineering & New York Genome Center, New York, USA
Industrial Panel Chairs:
Ilaria Bordino, Marco Firrincieli, Fabio Fumarola, and Francesco Gullo, UniCredit R&D
