dr. N.H. Jansen (Nils)

My group conducts broad foundational and application-driven research in artificial intelligence (AI). We take a broad stance on AI that brings together the areas of machine learning and formal methods, in particular formal verification. We tackle problems that are inspired by autonomous systems, industrial projects, and in particular planning problems in robotics. We use methods that span deep machine learning (ML) techniques using recurrent or graph neural networks or problems captured by partially observable Markov decision processes (POMDPs) in a data-driven context. The following goals are central to our efforts:

- Increase the dependability of AI in safety-critical environments.
- Render AI models robust against uncertain knowledge about the environment they operate in.
- Enhance the capabilities of verification to handle real-world problems using learning techniques.

In particular, we are interested in various aspects of Dependability and Safety in Artificial Intelligence, Intelligent Decision-making under Uncertainty and Safe Reinforcement Learning. A key aspect of our research is a thorough understanding of the (epistemic or aleatoric) uncertainty that may occur when AI systems operate in the real world.