About the Conference

The 5th Annual Conference on machine Learning, Optimization and Data science (LOD) is a single-track machine learning, computational optimization, data science conference that includes invited talks, tutorial talks, special sessions, industrial tracks, demonstrations and oral and poster presentations of refereed papers.

The International Conference on Machine Learning, Optimization, and Data Science (LOD) has established itself as a premier interdisciplinary conference in machine learning, computational optimization 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.

We invite submissions of papers, abstracts, posters and demos on all topics related to Machine learning, Optimization and Data Science including real-world applications for the Conference proceedings – Springer Lecture Notes in Computer Science.


The LOD Conference Manifesto

“The problem of understanding intelligence is said to be the greatest problem in science today and “the” problem for this century — as deciphering the genetic code was for the second half of the last one.
Arguably, the problem of learning represents a gateway to understanding intelligence in brains and machines, to discovering how the human brain works, and to making intelligent machines that learn from experience and improve their competences as children do.
In engineering, learning techniques would make it possible to develop software that can be quickly customized to deal with the increasing amount of information and the flood of data around us.”

The Mathematics of Learning: Dealing with Data
Tomaso Poggio (MOD 2015 Keynote Speaker)
Steve Smale

Papers submission

All papers must be submitted using EasyChair

Abstract & Late Breaking Paper Submission Deadline: June 4, 2019

Any questions regarding the submission process can be sent to conference organizers: lod@icas.xyz

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  • Early Registration as Presenting Author: June 30, 2019 
  • Early Registration: June 30, 2019 
  • Late Registration: From July 1, 2019 
  • On-Site Registration: September 10-13, 2019 


  • Abstract & Late Breaking Paper Submission Deadline: June 4, 2019 
  • Paper Submission Deadline: May 10, 2019 
  • Decision Notification to Authors: June 10, 2019
  • Camera Ready Submission Deadline: June 30, 2019 
  • Special Session Proposals: February 10, 2019 
  • Special Session Notification: February 25, 2019 

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Keynote Speakers

(random order)

General Chair

Vincenzo Sciacca
Almawave, Italy
Renato Umeton
Department of Informatics, Dana-Farber Cancer Institute, Boston, MA, USA & MIT, Cambridge, MA, USA

Program Chairs

Giovanni Giuffrida
University of Catania, Italy
Neodata Group
Panos Pardalos
Center for Applied Optimization, University of Florida, USA

Special Sessions Chair

Giorgio Jansen
University of Cambridge, UK
Salvatore Danilo Riccio
Queen Mary University of London, UK

Tutorial Sessions Chair

Vincenzo Sciacca
Almawave, Italy

Publicity Chair

Stefano Mauceri
NCRA, University College Dublin, Ireland

Industrial Session Chairs

Ilaria Bordino
UniCredit R&D, Italy
Marco Firrincieli
UniCredit R&D, Italy
Fabio Fumarola
University of Bari, Italy
Francesco Gullo
UniCredit R&D, Italy
Vincenzo Sciacca
Almawave, Italy

Steering Committee

Giuseppe Nicosia
Panos Pardalos


+39-0577-1521104 – info@lacertosadipontignano.com
Pontignano — Siena — Tuscany — Italy
Certosa di Pontignano

Past Editions

  • LOD 2018
    The Forth International Conference on Machine Learning, Optimization and Big Data
    Volterra – Tuscany – Italy
  • MOD 2017
    The Third International Conference on Machine Learning, Optimization and Big Data
    Volterra – Tuscany – Italy
  • MOD 2016
    The Second International Workshop on Machine learning, Optimization and big Data
    Volterra – Tuscany – Italy
  • MOD 2015
    International Workshop on Machine learning, Optimization and big Data
    Taormina – Sicily – Italy

LOD 2019 conference is organized as a non-profit event.