Dr Jacob Montiel

Postdoctoral Fellow (Computer Science)

Qualifications: PhD UPS-TPT

Contact Details

Room: FG.2.04
Phone: +64 7 837 9641

About Jacob

I am a Research Fellow in the Machine Learning Group at the University of Waikato. I am the lead developer and maintainer of scikit-multiflow, a machine learning framework for multi-label/multi-output stream data.

I worked at GE Aviation as Tech Lead in the development of embedded software for Diagnostics and Prognostics of Airborne Systems.

Recent Publications

  • Montiel, J., Halford, M., Mastelini, S. M., Bolmier, G., Sourty, R., Vaysse, R., . . . Bifet, A. (2021). River: Machine learning for streaming data in Python. Journal of Machine Learning Research, 22(10), 1-8. Retrieved from Open Access version:

  • Yogarajan, V., Montiel, J., Smith, T., & Pfahringer, B. (2021). Transformers for multi-label classification of medical text: an empirical comparison. In A. Tucker, P. Henriques Abreu, J. Cardoso, P. Pereira Rodrigues, & D. Riaño (Eds.), Proc 19th International Conference on Artificial Intelligence in Medicine (AIME 2021), LNCS 12721 (pp. 114-123). Virtual Event: Springer. doi:10.1007/978-3-030-77211-6_12

  • Togbe, M., Barry, M., Boly, A., Chabchoub, Y., Chiky, R., Montiel Lopez, J., & Vinh-Thuy, T. (2020). Anomaly detection for data streams based on isolation forest using scikit-multiflow. In O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Blečić, D. Taniar, . . . C. M. Torre (Eds.), Proc 20th International Conference on Computational Science and its Applications (ICCSA 2020) Part IV, LNCS 12252 (pp. 15-30). Caligari, Italy. doi:10.1007/978-3-030-58811-3_2

  • Montiel, J. (2020). Learning from evolving data streams. In Proceedings of the 19th Python in Science Conference (pp. 70-77). SciPy. doi:10.25080/majora-342d178e-00a

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