Christian’s research focuses on the intersection of machine learning and systems design. He explores how systems perspectives can help develop safe and reliable machine learning technologies, combining data-oriented architectures with techniques from service-oriented computing and self-adaptive systems.
Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective
Towards Better Data Discovery and Collection with Flow-Based Programming
Neurips Data-Centric AI Workshop (DCAI), :
An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context
1st International Conference on AI Engineering – Software Engineering for AI, :