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Personal profile

Personal profile

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

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:
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

Fingerprint Dive into the research topics where Meike Nauta is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 1 Similar Profiles
Time series Engineering & Materials Science
Neural networks Engineering & Materials Science
Large scale systems Engineering & Materials Science
Decision making Engineering & Materials Science
Websites Engineering & Materials Science
Statistical tests Engineering & Materials Science
Experiments Engineering & Materials Science
Failure modes Engineering & Materials Science

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Research Output 2017 2019

  • 2 Conference contribution
  • 1 Article
50 Downloads (Pure)

Causal Discovery with Attention-Based Convolutional Neural Networks

Nauta, M., Bucur, D. & Seifert, C., 7 Jan 2019, In : Machine Learning and Knowledge Extraction. 1, 1, p. 312-340 28 p.

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
Time series
Neural networks
Large scale systems
Decision making
Data mining

LIFT: Learning Fault Trees from Observational Data

Nauta, M., Bucur, D. & Stoelinga, M., 2018, Quantitative Evaluation of Systems: 15th International Conference, QEST 2018, Beijing, China, September 4-7, 2018, Proceedings. McIver, A. & Horvath, A. (eds.). Springer, (Lecture Notes in Computer Science; vol. 11024).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Open Access
Statistical tests
Failure modes
Learning systems
1 Citation (Scopus)
204 Downloads (Pure)

Detecting Hacked Twitter Accounts based on Behavioural Change

Nauta, M., Habib, M. B. & van Keulen, M., Apr 2017, Proceedings of the 13th International Conference on Web Information Systems and Technologies (WEBIST 2017). INSTICC Institute for Systems and Technologies of Information, Control and Communication, p. 19-31

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Open Access