| 2023 | DSN | Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning. | Janek Gro, Michael Kls, Lisa Jckel, Pascal Gerber |
| 2023 | PROFES | Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning. | Lisa Jckel, Michael Kls, Janek Gro, Pascal Gerber, Markus Scholz, Jonathan Eberle, Marc Teschner, Daniel Seifert, Richard Hawkins, John Molloy, Jens Ottnad |
| 2023 | SAFECOMP | Conformal Prediction and Uncertainty Wrapper: What Statistical Guarantees Can You Get for Uncertainty Quantification in Machine Learning? | Lisa Jckel, Michael Kls, Janek Gro, Pascal Gerber |
| 2022 | SAFECOMP | Architectural Patterns for Handling Runtime Uncertainty of Data-Driven Models in Safety-Critical Perception. | Janek Gro, Rasmus Adler, Michael Kls, Jan Reich, Lisa Jckel, Roman Gansch |
| 2021 | IJCAI | Using Complementary Risk Acceptance Criteria to Structure Assurance Cases for Safety-Critical AI Components. | Michael Klaes, Rasmus Adler, Lisa Jckel, Janek Gro, Jan Reich |
| 2021 | PROFES | Towards a Common Testing Terminology for Software Engineering and Data Science Experts. | Lisa Jckel, Thomas Bauer, Michael Kls, Marc P. Hauer, Janek Gro |
| 2020 | ICLR | Directional Message Passing for Molecular Graphs. | Johannes Klicpera, Janek Gro, Stephan Gnnemann |