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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.

Projects

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.

  • 3 Similar Profiles
Neural networks Engineering & Materials Science
Decision making Engineering & Materials Science
Time series Engineering & Materials Science
Textures Engineering & Materials Science
Experiments Engineering & Materials Science
Color Engineering & Materials Science
Large scale systems Engineering & Materials Science
Network architecture Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2017 2019

  • 2 Conference contribution
  • 2 Paper
  • 1 Article
57 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
File
Time series
Neural networks
Large scale systems
Decision making
Data mining

Evaluating CNN Interpretabilty on Sketch Classification

Theodorus, A., Nauta, M. & Seifert, C., 2019.

Research output: Contribution to conferencePaperAcademicpeer-review

Open Access
File
Textures
Color
Neural networks
Network architecture
Decision making

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

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

Research output: Contribution to conferencePaperAcademicpeer-review

Open Access
File
Neural networks
Visualization
Neurons
Decision making

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
Availability
Industry
1 Citation (Scopus)
238 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
File
Websites
Experiments
Malware