CA24136 - Interactions between Control Theory and Machine Learning (InterCoML)

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. In particular, it aims to
(i) strengthen the control-theoretical foundations of ML methods,
(ii) leverage modern ML tools to tackle complex and high-dimensional CT problems,
(iii) develop hybrid and data-driven models for highly complex application scenarios,
(iv) transform theoretical results into software solutions and practical implementations in industry and society.

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, which often work in parallel on similar problems but using different terminology and tools, and without sufficient communication with each other. This will allow the Action to combine different approaches, creating synergies that will benefit both sides, and leading to progress in both theoretical investigations and applications. 

Through the targeted transfer of knowledge and technology to the industrial sector, the Action will bring benefits to broader society. Focus will be given to implementation options in energy systems and personalised medicine, with the aim of improving the sustainability and environmental profile, as well as healthcare outcomes of European citizens.