InterCoML

Interactions between Control Theory and Machine Learning

Project Timeline: October 2025 – October 2029
Funding Body: European Cooperation in Science and Technology (COST Action CA24136)
Role: Management Committee Member

This Action will exploit the deep interconnections between Control Theory (CT) and Machine Learning (ML). It will boost applications of tools from CT to ML and vice versa, and explore the great applicative potential that can be released by combining these two rapidly evolving research areas.

Bringing together participants from multiple fields (mathematical analysis, numerical mathematics, control engineering, computer science, data science, etc.), the Action will foster interdisciplinary and cross-sector collaboration within a diverse group of experts from academia and industry. It will also combat fragmentation and communication barriers between the ML and CT communities.

  • ✓ Control Theory (CT): Providing rigorous foundations and stability guarantees.
  • ✓ Machine Learning (ML): Offering powerful data-driven tools for complex problems.
Project Aims
  • • Strengthening the control-theoretical foundations of ML methods.
  • • Leveraging modern ML tools to tackle complex and high-dimensional CT problems.
  • • Developing hybrid and data-driven models for highly complex application scenarios.
  • • Transforming theoretical results into software solutions and practical implementations.