We invite contributions from the IJCAI19-DSO and from the DSO stream at 30th EURO Conference to the Special Issue on Data Science meets Optimization of the journal Annals of Mathematics and Artificial Intelligence.

Guest editors:

• Prof. Dr. Patrick De Causmaecker (KU Leuven, BE)
• Dr. Michele Lombardi (University of Bologna, IT)
• Dr. Yingqian Zhang (TU Eindhoven, NL)

Important dates:

• Submission Deadline：October 1st, 2019
• Notification of status & acceptance or invitation for revision of paper: December 15th, 2019
• Revised manuscripts：February 15, 2020
• Final version of paper：April 30, 2020
• Anticipated publication：2020

Description and the topics of thes pecial issue:

We invite original and high quality work related to the theme of Data Science meets Optimization. Topics but are not limited to:

• Using optimization algorithms in developing Machine Learning models, e.g. formulating the problem of learning predictive models as mathematical optimization models, constraint programming (CP), or satisfiability (SAT). Tuning machine learning models using search algorithms and metaheuristics. Learning in the presence of constraints.
• Embedding methods, e.g. combining Machine Learning with Combinatorial Optimization, model transformations and solver selection, reasoning over Machine Learning models.
• Formal analysis of Machine Learning models via optimization or constraint satisfaction techniques, including safety checking and verification via SMT or MIP, generation of adversarial examples via similar combinatorial techniques.
• Applying Data Science and Machine Learning methods to solve Combinatorial Optimization problems, such as algorithm selection based on historical data, speeding up (or driving) the search process using Machine Learning, and handling uncertainties of prediction models for decision making.
• Computing explanations for ML model via techniques developed for optimization or constraint reasoning systems
• Applications of integration of techniques of Data Science and Optimization.

Submission Procedure

Details regarding the submission format and on-line submission site can be found at

http://www.editorialmanager.com/amai/,

and select the issue S697 Data science meets optimization.

All manuscriptsare subject to peer review. The refereeing will be at the same level as in any of the majorjournal publications in the area.