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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

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Research Output

  • 3 Conference contribution
  • 1 Paper
  • 1 Article

Evaluating CNN interpretability on sketch classification

Theodorus, A., Nauta, M. & Seifert, C., 31 Jan 2020, 12th International Conference on Machine Vision, ICMV 2019. Osten, W., Nikolaev, D. & Zhou, J. (eds.). SPIE Press, 114331Q. (Proceedings of SPIE - The International Society for Optical Engineering; vol. 11433).

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

Open Access
  • 3 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
  • 87 Downloads (Pure)

    Visualising the Training Process of Convolutional Neural Networks for Non-Experts

    Peters, M., Kempen, L., Nauta, M. & Seifert, C., 2019.

    Research output: Contribution to conferencePaper

    Open Access
  • 142 Downloads (Pure)

    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
  • 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): 19-31, 2017, Porto, Portugal. Majchrzak, T. A., Traverso, P., Krempels, K-H. & Monfort, V. (eds.). 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
  • 3 Citations (Scopus)
    709 Downloads (Pure)