Jacob Kamminga completed his Ph.D. in animal activity recognition at the University of Twente in 2020. He used machine learning models as activity classifiers to recognize various activities of animals using motion data on resource-constrained devices (edge-AI). Currently, he is studying active learning and human-in-the-loop AI training. Furthermore, he is interested in unsupervised representation learning to exploit unlabeled data. Other areas of his expertise are data acquisition, processing, and annotation. 

Since 2021 Jacob has been the digital species identification team leader within the ARISE biodiversity project. The digital species identification team builds services that support developing and deploying AI algorithms that detect and identify species from various digital media such as sound, images, and radar.


  • Computer Science

    • Activity Recognition
    • Classifier
    • Accuracy
    • Annotation
  • Earth and Planetary Sciences

    • Recognition
    • Detection
    • Biodiversity
  • Physics

    • Acoustics



ARISE: Building an infrastructure for species recognition and biodiversity monitoring in the Netherlands. van Ommen Kloeke, E., Huijbers, C., Beentjes, K., Kamminga, J. W., Bakker, P. A. J. & Kissling, W. D.https://doi.org/10.3897/biss.6.93613Improving the Annotation Efficiency for Animal Activity Recognition using Active LearningIn Measuring Behavior 2022: 12th International Conference on Methods and Techniques in Behavioral Research, and 6th Seminar on Behavioral Methods (pp. 51-58). Spink, S., Kamminga, J. W. & Kamilaris, A.https://doi.org/10.6084/m9.figshare.20066849

Research profiles

Courses academic year 2023/2024

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

Courses academic year 2022/2023


University of Twente

Zilverling (building no. 11)
Hallenweg 19
7522 NH Enschede

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