Thomas Hildebrandt

Thomas Hildebrandt is Professor of Software Engineering at the University of Copenhagen's Department of Computer Science and heads the Software, Data, People & Society section. He holds a PhD in computer science from Aarhus University and researches methods for reliable, flexible digital systems. A recurring question is how systems can adapt to changing workflows and rules, including after they have entered use.

His technical foundation is formal process models: precise descriptions of actions and the conditions governing their relationships. He initiated the research behind DCR technology and has contributed to its further development alongside research groups. This work connects software engineering with the digitalisation of legislation, business processes and case management, where both understandable rules and room for professional judgement matter.

As research lead for EcoKnow, he helped bring together universities, businesses and public organisations to support more flexible and transparent case management. The project's results included tools that make statutory actions and deadlines visible to caseworkers. A central distinction in his explanation of the project is that technology can support oversight and compliance without taking over the employee's decision or professional responsibility.

A more recent research direction explores the interplay between generative AI and explainable symbolic models. In 2025, he and Thiago Rocha Silva published a position paper on human-centric hybrid AI for no-code development. The paper describes a research direction in which users can express workflows in an understandable but formal language, connecting generative AI with models that can be examined and explained. This continues his work to bring together technical reliability and human understanding.

Selected experience

• Professor, section head and PhD in computer science.
• Formal process models and the research behind DCR technology.
• Leadership of EcoKnow on flexible, transparent case management supported by rules.
• Research into hybrid AI, explainable models and human-centric no-code development.