I am a PhD Candidate at the Data Science group of the University of Twente, the Netherlands. My research interests include explainable artificial intelligence, interpretable machine learning, deep learning, and causal discovery.

Daily life is increasingly governed by decisions made by algorithms due to the growing availability of big data sets. Most machine learning algorithms are black-box models, i.e. they give no insight into how they reach their outcomes which prevents users from trusting the model. If we cannot understand the reasons for their decisions, how can we be sure that the decisions are correct? What if they are wrong, discriminating or amoral?
I aim to create new machine learning methods that can explain their decision making process, in order for users to understand the reasons behind a prediction. Those explanations enable the user to check for correctness, fairness and robustness, and can also be useful for knowledge discovery.

Software developed for my published research is available online:

Neural Prototype Trees for interpretable fine-grained image recognition https://github.com/M-Nauta/ProtoTree
Discovering causal relationships between time series https://github.com/M-Nauta/TCDF
Learning Fault Trees to model system failures https://github.com/M-Nauta/LIFT