We invite contributions from the IJCAI21-DSO to the Special Issue on Data Science meets Optimization Vol. 2 of the journal Annals of Mathematics and Artificial Intelligence.

Guest editors:

• Prof. Dr. Patrick De Causmaecker (KU Leuven, BE)

• Dr. Tias Guns (KU Leuven, BE)

• Dr. Michele Lombardi (University of Bologna, IT)

• Dr. Yingqian Zhang (TU Eindhoven, NL)

Important dates:

• Submission Deadline：October 31, 2021

• Notification of status & acceptance or invitation for revision of paper: January 31, 2022

• Revised manuscripts：March 15, 2022

• Final version of paper：June 30, 2022

• Anticipated publication：2022

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:

• 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 including reinforcement learning, and handling uncertainties of prediction models for decision-making or neural combinatorial optimization.

• Using optimization algorithms for the development of Machine Learning models: formulating the problem of learning predictive models as MIP, constraint programming (CP), or satisfiability (SAT). Tuning Machine Learning models using search algorithms and meta-heuristics. Learning constraint models from empirical data.

• Embedding/encoding methods: combining Machine Learning with combinatorial optimization, model transformations and solver selection, reasoning over Machine Learning models. Introducing constraints in (hybrid) Machine Learning models as well as 'predict and optimize'.

• Formal analysis of Machine Learning models via optimization or constraint satisfaction techniques: safety checking and verification via SMT or MIP, generation of adversarial examples via similar combinatorial techniques.

• 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.