Jean-François BAFFIER

Email: [lastname]
Optimization for all, empowering networks!
"Optimization for All & Model as You Speak!" encapsulates my research goal: to democratize optimization through a high-performance, user-friendly framework. This work aims to bridge the gap between advanced algorithms and practical application, making constraint programming and optimization accessible to both general users and specialists alike. The framework is fine-tuned through its applications to network and cloud infrastructures.

Research Topics

Optimization is a cornerstone in decision-making processes across various domains—from logistics and healthcare to finance and engineering. While the academic landscape is replete with specialized algorithms and techniques to solve complex optimization problems, the practical implementation often poses a barrier to entry for non-specialists. Moreover, even within the specialized community, the rapid growth in computational approaches has led to a dispersion of tools that are often not interoperable, hindering their effective use. The primary aim of this research is to democratize the field of optimization by developing a semi-automated framework accessible to both general users and specialists. This framework will not only encapsulate state-of-the-art algorithms in constraint programming and optimization but will also offer an intuitive user interface for problem formulation and solution interpretation. In doing so, we aspire to make advanced optimization techniques more readily available for practical applications, thereby bridging the gap between academic research and real-world problems. By leveraging the Julia programming language, known for its high-performance capabilities in technical computing, the framework aims to offer both ease-of-use and computational efficiency. Hosted under the auspices of Internet Initiative Japan, this research benefits from a robust technological infrastructure and aims to contribute significantly to the society, offering optimized solutions for internet services and beyond.

  • Cloud Morphing
  • Semi-automated Optimization Framework
  • Network Flows
  • Data Compression
  • Data Structure


Journal Papers

  • Florian Richoux and Jean-François Baffier, "Automatic error function learning with interpretable compositional networks". Annals of Mathematics and Artificial Intelligence, 2023. (pdf, bib, code/data)

Conference Papers

  • Florian Richoux, Jean-François Baffier, and Philippe Codognet, "Learning QUBO Models for Quantum Annealing: A Constraint-based Approach". In Proceedings of the 2023 International Conference on Computational Science ({ICCS}), 153--167, 2023. Springer LNCS. (pdf, bib, code/data)
  • Kenjiro Cho and Jean-François Baffier, "An Autonomous Resource Management Model towards Cloud Morphing". In Proceedings of the 6th International Workshop on Edge Systems, Analytics and Networking, 2023. (pdf, bib, code/data)
  • Florian Richoux and Jean-François Baffier, "Error function learning with interpretable compositional networks for constraint-based local search". In Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021. (pdf, bib, code/data)


  • ConstraintLearning.jl: a Julia package to learn about optimization constraints (Error Functions, QUBO matrices)
  • PerfChecker.jl: a Julia package to check the performance of software over versioning
  • CBLS.jl: a JuMP interface for a Constraint-based Local Search solver in Julia
  • CompressedStacks.cpp: a C++ library to compress stacks on external memory
  • StaticWebPages.jl: a web programming agnostic web page generator for researchers
  • Bibliography.jl: a Julia package to manage bibliographies, including BibTeX


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