Data Science Meets Optimisation

An IJCAI 2021 Workshop (id: 38)


  • Extended submission deadline till May 31!

  • Co-located with the IJCAI competition "AI for TSP" (

  • We are delighted to announce our keynote speakers!

Paul Grigas
(Assistant Professor, University of California, Berkeley)

Patrick Henne (CTO, ORTEC)

Important Dates

  • May 31, 2021: deadline for submitting contributions (extended!)

  • June 15, June 22, 2021: notification of acceptance

  • Workshop (virtual):

    • Aug 19 (2PM – 8PM) UTC

    • Aug 20 (2PM – 8PM) UTC

About the Workshop

Data science and optimization are closely related. On the one hand, many problems in data science can be solved using optimizers, on the other hand optimization problems stated through classical models such as those from mathematical programming cannot be considered independent of historical data. Examples are ample: Machine Learning (ML) often relies on optimization techniques such as linear or integer programming; reasoning systems have been applied to constrained pattern and sequence mining tasks; a parallel development of metaheuristic approaches has taken place in the domains of data mining and machine learning; methods aimed at high level combinatorial optimization have been shown to strongly profit from configuration, algorithm selection and tuning tools building on historical data; ML models can be embedded in combinatorial optimization problems to address hard-to-model systems, or for validation of the ML model itself; “predict, then optimize” scenarios can be dealt with in an integrated fashion to improve considerably the solution quality.

This workshop continues on DSO@IJCAI2020 (online) and the DSO@IJCAI2019 workshop at the International Joint Conference on Artificial Intelligence 2019 in Macao. The DSO workshop is closely related to the DSO working group of The Association of European Operational Research Societies (EURO). In addition to DSO@FAIM 2018, previous related activities include: DSO@IJCAI-ECAI 2018 (Stockholm); stream at EURO 2018 (Valencia); stream at IFORS 2017 (Quebec); workshop at CPAIOR 2017 (Padua); workshop at CEC 2017 (San Sebastian); the foundational workshop (Leuven). More information about the previous editions of DSO is available at

Aim AND Topics

The aim of the workshop is to organize an open discussion and exchange of ideas by researchers from Data Science, Constraint Optimization and Operations Research in order to identify how techniques from these fields can benefit each other. The program committee 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 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.


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

  • Submission of original work up to 6 pages in length (+ references).

  • Submission of work in progress with preliminary results, and position papers, up to 4 pages in length (+ references).

  • Published journal/conference papers in the form of a 2-pages extended abstracts.

Submission should be prepared following the IJCAI formatting instructions at:

The review process is single-blind. The program committee will select the papers to be presented at the workshop according to their suitability to the aims.

Selected contributors will be invited to submit extended articles to a special issue of Annals of Mathematics and Artificial Intelligence.

Submissions through:

Format and Schedule

The detailed schedule will be made available after the list of accepted papers is finalized.


The workshop co-chairs are:

  • Patrick De Causmaecker (KU Leuven, BE)

  • Tias Guns (Vrije Universiteit Brussel, BE)

  • Michele Lombardi (University of Bologna, IT)

  • Yingqian Zhang (TU Eindhoven, NL)


EURO Working Group on DSO:

Endorsed by: