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

  • Barry, M., Bifet, A., Chiky, R., Montiel, J., & Tran, V. T. (2021). Challenges of Machine Learning for Data Streams in the Banking Industry. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13147 LNCS (pp. 106-118). Virtual Even. doi:10.1007/978-3-030-93620-4_9

  • 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

  • Gomes, H. M., Montiel, J., Mastelini, S. M., Pfahringer, B., & Bifet, A. (2020). On ensemble techniques for data stream regression. In Proc 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). Glasgow, UK: IEEE. doi:10.1109/IJCNN48605.2020.9206756

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