Are you working at the intersection of data science, machine learning, and optimization? The Data Science Meets Optimization (DSO 2026) workshop provides a friendly, discussion-driven forum for researchers and practitioners to exchange ideas and foster collaborations.

We welcome both published and unpublished work, including ongoing research, recent results, and forward-looking ideas. The goal is to exchange insights, spark collaborations, and strengthen the research community.

The workshop invites submissions that include but are not limited to the following topics:

* 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 (deep) reinforcement learning, and handling uncertainties of prediction models for decision-making.

* 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 ML models using search algorithms and meta-heuristics. Learning constraint models from empirical data.

* LLM-guided optimization and optimization for LLMs: prompting techniques for reasoning, LLM-assisted modelling for constrained optimization, LLM-guided heuristics and search strategies, constrained optimization for improving LLM reasoning, LLM-based end-to-end neural solutions to optimization problems.

* Embedding/encoding methods: combining ML with combinatorial optimization, model transformations and solver selection, reasoning over ML models. Introducing constraints in (hybrid) ML models as well as decision-focussed learning.

* Formal analysis of ML 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.

* Theoretical or empirical research on generalization and robustness of ML models to improve optimization performance in out-of-distribution and worst-case scenarios.

* Multiple model learning for ensemble combinatorial optimization with mixed input of images, graphs, or programming language.

* Applications of integrations of techniques of data science and optimization.

Authors are invited to send a contribution in the IJCAI proceedings format, in the form of:

(a) Long paper: Submission of original work up to 7 pages in length (plus max 2 pages of references).

(b) Short paper: Submission of work-in-progress with preliminary results, and of position papers, up to 4 pages in length (plus max 1 page of references).

(c) Extended abstract: Published journal/conference papers in the form of a 2-page extended abstract.


Submission should be prepared following the IJCAI formatting instructions at:
https://www.ijcai.org/authors_kit
The review process is single-blind. The programme committee will select the papers to be presented at the workshop according to their suitability to the aims.

Submission link: https://chairingtool.com/conferences/dso2026/main-track?role=author