Call for Papers

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.

MOD 2017 will be held in Volterra (Pisa) – Tuscany, Italy, from September 14 to 17, 2017. 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 by Springer – Lecture Notes in Computer Science (LNCS).

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

Topics of Interest

The last five-year period has seen a impressive revolution in the theory and application of  machine learning 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.

MOD 2017 Paper Format

Please prepare your paper in English using the Springer – Lecture Notes in Computer Science (LNCS) template, which is available here. Papers must be submitted in PDF.

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; any format). 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.

Following the tradition of MOD, we expect high-quality papers in terms of their scientific contribution, rigor, correctness, quality of presentation and reproducibility of experiments.

Accepted papers must contain significant novel results. Results can be either theoretical or empirical. Results will be judged on the degree to which they have been objectively established and/or their potential for scientific and technological impact.

Proceedings by Springer – Lecture Notes in Computer Science

All accepted long papers  will be published in a volume of the series on 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 poster presentations) will be published on the MOD 2017 web site.

MOD Conference past editions:

  • MOD 2016 Proceedings, Springer LNCS 10122, Machine Learning, Optimization, and Big Data Second International Workshop, MOD 2016, Volterra, Italy, August 26-29, 2016, Revised Selected Papers  Editors: Pardalos, P.M., Conca, P., Giuffrida, G., Nicosia, G. (Eds.).
  • MOD 2015 Proceedings, Springer LNCS 9432, Machine Learning, Optimization, and Big Data First International Workshop, MOD 2015, Taormina, Sicily, Italy, July 21-23, 2015.

Submission System


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 year 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 via
  • Submission deadline: 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.


All papers must be submitted using EasyChair. The submission Web site for MOD 2017 is

Paper Submission Deadline:  May  31, 2017


Any questions regarding the submission process can be sent to  conference organizers:


Important Dates

Paper Submission Deadline:  May  31, 2017

Decision Notification to Authors: July 1, 2017

Camera Ready Submission Deadline: July 15, 2017

Deadline for early Registration as Presenting Author: July 15, 2017

Deadline for early Registration: August 14, 2017

Late registration: August 15 – September 17, 2017

On-Site registration: September 14-17, 2017

MOD 2017  Conference: September 14-17, 2017


MOD 2017 Conference will showcase a wide range of topics in Machine Learning, Optimization and Big Data

Topics in Machine Learning

  • Active Learning
  • Analogical learning methods
  • Applications
  • Approximate Inference
  • Audio and Speech Processing
  • Auditory Perception and Modelling
  • Automated knowledge acquisition
  • Bandit Algorithms
  • Bayesian Non-parametrics
  • Bayesian Theory
  • Belief Propagation
  • Bioinformatics
  • Biologically inspired machine learning algorithms
  • Brain Imaging
  • Brain-computer Interfaces and Neural Prostheses
  • Case-based methods
  • Causality
  • Classification, regression, recognition, and prediction
  • Clustering
  • Cognitive Science
  • Collaborative Filtering and Recommender Systems
  • Component Analysis (ICA, PCA, CCA, FLDA)
  • Compressed Sensing and Sparse Reconstruction
  • Computational Neural Models
  • Computer Vision
  • Connectionist networks
  • Control Theory
  • Data mining
  • Deep Learning
  • Density Estimation
  • Design and diagnosis
  • Ensemble Methods and Boosting
  • Evolution-based machine learning methods
  • Exact Inference
  • Explanation-based learning
  • Feature Learning
  • Frequentist Statistics
  • Game playing
  • Game Theory and Computational Economics
  • Gaussian Processes
  • Graph Based Learning
  • Graphical Models
  • Hardware for Machine Learning
  • Image Segmentation
  • Inductive logic programming
  • Industrial, financial, and scientific applications of all kinds
  • Information Retrieval
  • Information Theory
  • Kernel Methods
  • Knowledge Representation and Acquisition
  • Language (Cognitive Science)
  • Large Margin Methods
  • Large Scale Learning and Big Data
  • Learning decision and regression trees and rules
  • Learning from instruction;
  • Learning in integrated architectures
  • Learning Theory
  • MCMC
  • Matrix Factorization
  • Missing Data
  • Model Selection and Structure Learning
  • Motor Control
  • Multi-Agent Systems
  • Multi-strategy learning
  • Multi-task and Transfer Learning
  • Music Modelling and Analysis
  • Natural Language Processing
  • Natural Scene Statistics
  • Neural Coding
  • Neural Networks
  • Neuroscience
  • Nonlinear Dimension Reduction and Manifold Learning
  • Object Recognition
  • Online Learning
  • Other Supervised Learning Methods
  • Other Unsupervised Learning Methods
  • Privacy, Anonymity, and Security
  • Probabilistic Models and Methods
  • Probabilistic networks and other statistical models
  • Problem solving and planning
  • Quantitative Finance and Econometrics
  • Ranking and Preference Learning
  • Reasoning and inference
  • Regression
  • Reinforcement Learning Algorithms
  • Reinforcement Learning (Cognitive/Neuroscience)
  • Relational Models
  • Representation Learning
  • Robotics
  • Robotics and control
  • Scientific discovery
  • Semi-Supervised Learning
  • Signal Processing
  • Similarity and Distance Learning
  • Social Networks
  • Sparse Coding
  • Sparsity and Feature Selection
  • Spectral Methods
  • Speech and Signal Processing
  • Speech Recognition
  • Statistical Learning Theory
  • Stochastic Methods
  • Structured Prediction
  • Supervised Learning
  • Support Vector Machines
  • Systems Biology
  • Text Mining
  • Theoretical Neuroscience
  • Time Series Analysis
  • Topic Models
  • Unsupervised learning methods
  • Variational Inference
  • Video, Motion and Tracking
  • Vision and speech perception
  • Visual Features
  • Visual Perception
  • Visualization of patterns in data
  • Web Applications, Web mining and Internet Data

Topics in Optimization

  • Biological inspired Optimization
  • Combinatorial optimization
  • Convex Optimization
  • Derivative-based Optimization
  • Derivative-free Optimization
  • Deterministic Global Optimization
  • Discrete-Continuous Nonlinear Optimization
  • Evolutionary Optimization
  • Geometric Programming
  • Global Optimization
  • Integer Programming
  • Financial Optimization
  • Large Scale Optimization
  • Local versus Global Optimization
  • Metaheuristics and Benchmarking
  • Mixed-integer Nonlinear Optimization
  • Multiobjective Optimization
  • Nonlinear Programming
  • Nonlinear Optimization
  • NP Complete Problems
  • Optimal Control
  • Other Optimization Methods
  • Polynomial Optimization
  • Quantum Optimization
  • Randomized Optimization
  • Routing
  • Scheduling
  • Smoothed Analysis
  • Stochastic Optimization

Topics in Big Data Science

  • Advanced database and Web Applications
  • Algorithms and Programming Techniques for Big Data Processing
  • Algorithms and Systems for Big Data Search
  • Anomaly and APT Detection in Very Large Scale Systems
  • Anomaly Detection in Very Large Scale Systems
  • Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment
  • Big Data Analytics and Metrics
  • Big Data Analytics in Government, Public Sector and Society in General
  • Big Data Analytics in Small Business Enterprises
  • Big Data Applications
  • Big Data Architectures
  • Big Data in Business Performance Management
  • Big Data Models and Algorithms
  • Big Data as a Service
  • Big Data Open Platforms
  • Big Data in Mobile and Pervasive Computing
  • Big Data Foundations
  • Big Data Industry Standards
  • Big Data Infrastructure
  • Big Data Management
  • Big Data Persistence and Preservation
  • Big Data Quality and Provenance Control
  • Big Data in Enterprise Management Models and Practices
  • Big Data in Government Management Models and Practices
  • Big Data in Smart Planet Solutions
  • Big Data for Enterprise Transformation
  • Big Data Protection, Integrity and Privacy
  • Big Data Encryption
  • Big Data Search and Mining
  • Big Data Security & Privacy
  • Big Data for Enterprise, Government and Society
  • Big Data Economics
  • Big Data for Business Model Innovation
  • Big Data for Vertical Industries (including Government, Healthcare, and Environment)
  • Big Data Search Architectures, Scalability and Efficiency
  • Big Data Toolkits
  • Cloud Computing Techniques for Big Data
  • Cloud/Grid/Stream Computing for Big Data
  • Cloud/Grid/Stream Data Mining- Big Velocity Data
  • Collaborative Threat Detection using Big Data Analytics
  • Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication
  • Computational Modeling and Data Integration
  • Crowdsourcing
  • Data Acquisition, Integration, Cleaning, and Best Practices
  • Data and Information Quality for Big Data
  • Database Management Challenges: Architecture, Storage, User Interfaces
  • Data Management for Mobile and Pervasive Computing
  • Data Management in the Social Web
  • Data Preservation
  • Data Protection, Integrity and Privacy Standards and Policies
  • Data Provenance
  • Distributed, and Peer-to-peer Search
  • Energy-efficient Computing for Big Data
  • Experiences with Big Data Project Deployments
  • Foundational Models for Big Data
  • HCI Challenges for Big Data Security & Privacy
  • Heterogeneous and Multi-structured Data Integration
  • High Performance Cryptography
  • High Performance/Parallel Computing Platforms for Big Data
  • Interfaces to Database Systems and Analytics Software Systems Information Integration
  • Intrusion Detection for Gigabit Networks
  • Large-scale Recommendation Systems and Social Media Systems
  • Large-scale Social Media and Recommendation Systems
  • Link and Graph Mining
  • Machine learning based on Big Data
  • Management Issues of Social Network Big Data
  • Mobility and Big Data
  • Models and Languages for Big Data Protection
  • Multimedia and Multi-structured Data- Big Variety Data
  • New Computational Models for Big Data
  • New Data Standards
  • New Programming Models for Big Data beyond Hadoop/MapReduce, STORM
  • Novel Data Model and Databases for Emerging Hardware
  • Novel Theoretical Models for Big Data
  • Privacy Preserving Big Data Analytics
  • Privacy Threats of Big Data
  • Programming Models & Environments for Cluster, Cloud, & Grid Computing to Support Big Data
  • Real-life Case Studies of Value Creation through Big Data Analytics
  • Representation Formats for Multimedia Big Data
  • Scientific Applications of Big Data
  • Security Applications of Big Data
  • Semantic-based Data Mining and Data Pre-processing
  • SME-centric Big Data Analytics
  • Sociological Aspects of Big Data Privacy
  • Social Web Search and Mining
  • Software Systems to Support Big Data Computing
  • Software Techniques and Architectures in Cloud/Grid/Stream Computing
  • Spatiotemporal and Stream Data Management
  • Threat Detection using Big Data Analytics
  • Visualization Analytics for Big Data
  • Visualizing Large Scale Security Data
  • Web Search Distributed and Peer-to-peer Search