Pernille Matthews' PhD defence "Explaining patterns and outliers in machine learning models"

On Wednesday, April 29th 2026, Pernille Matthews will defend her PhD thesis Structure-Guided Explainability: From Regional to Counterfactual Explanations.

Summary:
Machine learning models can detect patterns in large datasets, but understanding why certain observations stand out as unusual or belong to particular groups remains challenging. While many methods identify outliers or clusters of data, they often provide little insight into the reasons behind these patterns.

During her PhD studies, Pernille Matthews developed methods that leverage the internal structure of well-known machine learning algorithms to gain deeper insight into data. By leveraging this structure, the research makes it possible to identify coherent groups of explanations and better understand why certain observations appear as outliers.

The PhD study was completed at the Department of Computer Science, Faculty of Natural Sciences, Aarhus University.

This summary was prepared by the PhD student.