We invite contributions from the IJCAI24-DSO to the Collection on Data Science meets Optimization of the journal Annals of Mathematics and Artificial Intelligence.  

Guest editors:

• Dr. Yaoxin Wu (TU Eindhoven, NL) 

• Dr. Jayanta Mandi (KU Leuven, BE)

• Prof. LAU Hoong Chuin (Singapore Management University)

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

• Dr. Neil Yorke-Smith (TU Delft, NL)

• Dr. Yingqian Zhang (TU Eindhoven, NL) 

Important dates:

• Submission Deadline：November 15 December 15, 2024 (Extended)

• Notification of status & acceptance or invitation for revision of paper: March 15, 2025

• Revised manuscripts：April 30, 2025

• Final version of paper：August 15, 2025

• Anticipated publication：2025

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 https://link.springer.com/collections/aaddibbefi

All manuscripts are subject to peer review. The refereeing will be at the same level as in any of the major journal publications in the area.