Data Science Meets Optimisation

Important Dates

• 24 May 2026: Deadline for submission (23:59 Anywhere on Earth)

• 1 June 2026: Notification of acceptance

• 15-17 August 2026: Workshop date 

About the Workshop

Data science and optimization are tightly intertwined. Many problems in data science can be solved using optimizers. On the other side, most optimization problems stated through classical models such as those from mathematical programming cannot be considered independent of historical data. Examples are ample: Methods aimed at high-level combinatorial optimization have been shown to strongly benefit from configuration, algorithm selection and tuning tools that are learned from historical data; Machine Learning (ML) often relies on optimization techniques such as linear or integer programming, and increasingly so for verification and optimal decision trees; Metaheuristic approaches characterized by learning components are commonplace in mathematical optimization; Black-box optimization makes heavy use of machine learning; deep learning is increasingly adopted to predict solutions to combinatorial problems (such as routing and scheduling problems). Furthermore, ML models are embedded into the combinatorial optimization pipeline to address hard-to-model systems, to validate the ML model itself, or intertwined in methodologies like decision-focused learning. Not least, LLMs are used to help formulate and solve optimization problems, and optimization techniques can help verify LLM outputs, for example.

This workshop on the close relationship and interplay between data science and optimization continues on the series of workshop at previous IJCAI and other conferences, DSO@ECML-PKDD2025, DSO@IJCAI2024, DSO@IJCAI2022, DSO@IJCAI2021, DSO@IJCAI2020, DSO@IJCAI2019, and the inaugural DSO@IJCAI-ECAI workshop in 2018. The DSO workshop is closely related to the DSO working group of The Association of European Operational Research Societies (EURO) with yearly streams and workshops at major OR conferences such as IFORS 2025 in Vienna, EURO 2024 in Copenhagen, EURO 2022 in Espoo, EURO2021 in Athens, IFORS 2021 virtual, EURO 2019 in Dublin, EURO 2018 in Valencia, IFORS 2017 in Quebec, CPAIOR 2017 in Padua, and CEC 2017 in San Sebastian. More information about the previous editions of DSO is available at https://www.euro-online.org/websites/dso/.

Topics

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.

Submission

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

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

• 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).

• 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

Workshop Chairs

PAST EDITIONS

• 2025 @ ECML-PKDD2025: https://sites.google.com/view/dso-workshopecml-pkdd-2025

• 2024 @ IJCAI2024: https://sites.google.com/view/ijcai-2024-dso-workshop

• 2022 @ IJCAI2022: https://sites.google.com/view/ijcai2022dso/

• 2021 @ IJCAI2021: https://sites.google.com/view/ijcai2021dso

• 2020 @ IJCAI2020: https://sites. google.com/view/ijcai-2020-dso-workshop

• stream at IFORS 2020 (Seoul, moved to 2021) here

• 2019 @ IJCAI19: https://sites.google.com/view/ijcai2019dso

• 2018 @ FAIM19 https://www.faim2018.org

• 2018 @ IJCAI-ECAI18 (Stockholm) here

• Stream at EURO 2018 (Valencia)  here

• Stream at IFORS 2017 (Quebec) here

• Workshop at CPAIOR17 & CEC17 here

• Foundational workshop (Leuven) here

EURO Working Group on DSO: https://www.euro-online.org/websites/dso/